KaggleCompetition- Microsoft Malware prediction AUC score-0.676¶

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

df_dtype={'MachineIdentifier': 'category',
 'ProductName': 'category',
 'EngineVersion': 'category',
 'AppVersion': 'category',
 'AvSigVersion': 'category',
 'Platform': 'category',
 'Processor': 'category',
 'OsVer': 'category',
 'OsPlatformSubRelease': 'category',
 'OsBuildLab': 'category',
 'SkuEdition': 'category',
 'PuaMode': 'category',
 'SmartScreen': 'category',
 'Census_MDC2FormFactor': 'category',
 'Census_DeviceFamily': 'category',
 'Census_ProcessorClass': 'category',
 'Census_PrimaryDiskTypeName': 'category',
 'Census_ChassisTypeName': 'category',
 'Census_PowerPlatformRoleName': 'category',
 'Census_InternalBatteryType': 'category',
 'Census_OSVersion': 'category',
 'Census_OSArchitecture': 'category',
 'Census_OSBranch': 'category',
 'Census_OSEdition': 'category',
 'Census_OSSkuName': 'category',
 'Census_OSInstallTypeName': 'category',
 'Census_OSWUAutoUpdateOptionsName': 'category',
 'Census_GenuineStateName': 'category',
 'Census_ActivationChannel': 'category',
 'Census_FlightRing': 'category',
 'RtpStateBitfield': 'float16',
 'DefaultBrowsersIdentifier': 'float16',
 'AVProductsInstalled': 'float16',
 'AVProductsEnabled': 'float16',
 'OrganizationIdentifier': 'float16',
 'GeoNameIdentifier': 'float16',
 'IsProtected': 'float16',
 'SMode': 'float16',
 'IeVerIdentifier': 'float16',
 'Firewall': 'float16',
 'Census_OEMNameIdentifier': 'float16',
 'Census_ProcessorCoreCount': 'float16',
 'Census_ProcessorManufacturerIdentifier': 'float16',
 'Census_ProcessorModelIdentifier': 'float16',
 'Census_InternalPrimaryDiagonalDisplaySizeInInches': 'float16',
 'Census_InternalPrimaryDisplayResolutionHorizontal': 'float16',
 'Census_InternalPrimaryDisplayResolutionVertical': 'float16',
 'Census_OSInstallLanguageIdentifier': 'float16',
 'Census_IsFlightingInternal': 'float16',
 'Census_IsFlightsDisabled': 'float16',
 'Census_ThresholdOptIn': 'float16',
 'Census_FirmwareManufacturerIdentifier': 'float16',
 'Census_IsWIMBootEnabled': 'float16',
 'Census_IsVirtualDevice': 'float16',
 'Census_IsAlwaysOnAlwaysConnectedCapable': 'float16',
 'Wdft_IsGamer': 'float16',
 'Wdft_RegionIdentifier': 'float16',
 'AVProductStatesIdentifier': 'float32',
 'CityIdentifier': 'float32',
 'UacLuaenable': 'float32',
 'Census_OEMModelIdentifier': 'float32',
 'Census_PrimaryDiskTotalCapacity': 'float32',
 'Census_SystemVolumeTotalCapacity': 'float32',
 'Census_TotalPhysicalRAM': 'float32',
 'Census_InternalBatteryNumberOfCharges': 'float32',
 'Census_FirmwareVersionIdentifier': 'float32',
 'IsBeta': 'int32',
 'IsSxsPassiveMode': 'int32',
 'HasTpm': 'int32',
 'CountryIdentifier': 'int32',
 'LocaleEnglishNameIdentifier': 'int32',
 'OsBuild': 'int32',
 'OsSuite': 'int32',
 'AutoSampleOptIn': 'int32',
 'Census_HasOpticalDiskDrive': 'int32',
 'Census_OSBuildNumber': 'int32',
 'Census_OSBuildRevision': 'int32',
 'Census_OSUILocaleIdentifier': 'int32',
 'Census_IsPortableOperatingSystem': 'int32',
 'Census_IsSecureBootEnabled': 'int32',
 'Census_IsTouchEnabled': 'int32',
 'Census_IsPenCapable': 'int32',
 'HasDetections': 'int32'}

%%time
df=pd.read_csv("train.csv",dtype=df_dtype)#AUC of 6.75 for 5000000

Wall time: 4min 21s

df_dtype={'MachineIdentifier': 'category',
 'ProductName': 'category',
 'EngineVersion': 'category',
 'AppVersion': 'category',
 'AvSigVersion': 'category',
 'Platform': 'category',
 'Processor': 'category',
 'OsVer': 'category',
 'OsPlatformSubRelease': 'category',
 'OsBuildLab': 'category',
 'SkuEdition': 'category',
 'PuaMode': 'category',
 'SmartScreen': 'category',
 'Census_MDC2FormFactor': 'category',
 'Census_DeviceFamily': 'category',
 'Census_ProcessorClass': 'category',
 'Census_PrimaryDiskTypeName': 'category',
 'Census_ChassisTypeName': 'category',
 'Census_PowerPlatformRoleName': 'category',
 'Census_InternalBatteryType': 'category',
 'Census_OSVersion': 'category',
 'Census_OSArchitecture': 'category',
 'Census_OSBranch': 'category',
 'Census_OSEdition': 'category',
 'Census_OSSkuName': 'category',
 'Census_OSInstallTypeName': 'category',
 'Census_OSWUAutoUpdateOptionsName': 'category',
 'Census_GenuineStateName': 'category',
 'Census_ActivationChannel': 'category',
 'Census_FlightRing': 'category',
 'RtpStateBitfield': 'float16',
 'DefaultBrowsersIdentifier': 'float16',
 'AVProductsInstalled': 'float16',
 'AVProductsEnabled': 'float16',
 'OrganizationIdentifier': 'float16',
 'GeoNameIdentifier': 'float16',
 'IsProtected': 'float16',
 'SMode': 'float16',
 'IeVerIdentifier': 'float16',
 'Firewall': 'float16',
 'Census_OEMNameIdentifier': 'float16',
 'Census_ProcessorCoreCount': 'float16',
 'Census_ProcessorManufacturerIdentifier': 'float16',
 'Census_ProcessorModelIdentifier': 'float16',
 'Census_InternalPrimaryDiagonalDisplaySizeInInches': 'float16',
 'Census_InternalPrimaryDisplayResolutionHorizontal': 'float16',
 'Census_InternalPrimaryDisplayResolutionVertical': 'float16',
 'Census_OSInstallLanguageIdentifier': 'float16',
 'Census_IsFlightingInternal': 'float16',
 'Census_IsFlightsDisabled': 'float16',
 'Census_ThresholdOptIn': 'float16',
 'Census_FirmwareManufacturerIdentifier': 'float16',
 'Census_IsWIMBootEnabled': 'float16',
 'Census_IsVirtualDevice': 'float16',
 'Census_IsAlwaysOnAlwaysConnectedCapable': 'float16',
 'Wdft_IsGamer': 'float16',
 'Wdft_RegionIdentifier': 'float16',
 'AVProductStatesIdentifier': 'float32',
 'CityIdentifier': 'float32',
 'UacLuaenable': 'float32',
 'Census_OEMModelIdentifier': 'float32',
 'Census_PrimaryDiskTotalCapacity': 'float32',
 'Census_SystemVolumeTotalCapacity': 'float32',
 'Census_TotalPhysicalRAM': 'float32',
 'Census_InternalBatteryNumberOfCharges': 'float32',
 'Census_FirmwareVersionIdentifier': 'float32',
 'IsBeta': 'int32',
 'IsSxsPassiveMode': 'int32',
 'HasTpm': 'int32',
 'CountryIdentifier': 'int32',
 'LocaleEnglishNameIdentifier': 'int32',
 'OsBuild': 'int32',
 'OsSuite': 'int32',
 'AutoSampleOptIn': 'int32',
 'Census_HasOpticalDiskDrive': 'int32',
 'Census_OSBuildNumber': 'int32',
 'Census_OSBuildRevision': 'int32',
 'Census_OSUILocaleIdentifier': 'int32',
 'Census_IsPortableOperatingSystem': 'int32',
 'Census_IsSecureBootEnabled': 'int32',
 'Census_IsTouchEnabled': 'int32',
 'Census_IsPenCapable': 'int32'}

%%time
df_test=pd.read_csv("test.csv",dtype=df_dtype)#AUC of 6.46 for 5000000

Wall time: 4min 16s

solution=pd.DataFrame()
solution['MachineIdentifier']=df_test['MachineIdentifier']

df.shape

(8921483, 83)

df.memory_usage(deep=True).sum()

2861438459

df.head()

df.describe().T

print("Number of Columns : ",len(df.columns))
print("Number of Rows : ",len(df))

Number of Columns :  83
Number of Rows :  8921483

l=df.columns[df.isnull().any()].tolist()

drop_list=[]
delete_rows_list=[]
treat_list=[]
for column in l:
    #print("Number of Null Values for : "+str(column)+" "+str(df[column].isnull().sum())+" "+str(df[column].isnull().sum()*100/len(df))+"%")
    if df[column].isnull().sum()*100/len(df)>=70:#dropping columns with more than 70% of missing values
        drop_list.append(column)
    elif df[column].isnull().sum()*100/len(df)<=30:#Deleting rows having columns upto 30% missing values
        delete_rows_list.append(column)
    else:#Treating null values in the remainder of the columns
        treat_list.append(column)

df.drop(columns=drop_list,axis=1,inplace=True)#Dropping columns with more than 60% of missing values

df_test.drop(columns=drop_list,axis=1,inplace=True)#Dropping columns with more than 60% of missing values

#df.dropna(subset=delete_rows_list,axis=0,inplace=True)

import statistics

statistics.mode(df.SmartScreen)

'RequireAdmin'

df.SmartScreen.fillna(statistics.mode(df.SmartScreen),inplace=True)

#df.OrganizationIdentifier.fillna(np.nanmedian(df.OrganizationIdentifier),inplace=True)

df.shape

(8921483, 78)

t=df.isnull().sum().reset_index().sort_values(ascending=False,by=[0])#.plot(kind='barh',figsize=(12,7))

fig,ax=plt.subplots(figsize=(12,7))
sns.barplot(x=t[t[0]>0][0],y=t[t[0]>0]['index'],ax=ax)
ax.set_title("Missing Values in features")
ax.set_xlabel("Number of Missing Values")
ax.set_ylabel("Features")
plt.show()

df=df[~df.SmartScreen.isin(['&#x02;','&#x01;','&#x03;'])]

df.shape

(8920731, 78)

df['SmartScreen']=df.SmartScreen.str.lower()
df_test['SmartScreen']=df_test.SmartScreen.str.lower()

df=df[~df.SmartScreen.isin(['promt'])]

df=df[~df.SmartScreen.isin(['00000000'])]

df=df[~df.SmartScreen.isin(['enabled'])]

df=df[df.SmartScreen!='0']

df.SmartScreen.value_counts()

requireadmin    7493205
existsnotset    1046183
off              187907
warn             135484
prompt            34534
block             22533
on                  878
Name: SmartScreen, dtype: int64

df_test.SmartScreen.value_counts()

requireadmin     3413570
existsnotset      600446
off               163161
warn              125926
prompt             28889
block              21242
on                   939
&#x02;               404
&#x01;               267
0                      3
promprt                1
deny                   1
of                     1
requiredadmin          1
Name: SmartScreen, dtype: int64

df_test['SmartScreen']=df_test.SmartScreen.str.replace('&#x02;','existsnotset')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('&#x01;','existsnotset')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('0','off')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('deny','existsnotset')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('requiredadmin','existsnotset')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('of','existsnotset')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('promprt','existsnotset')

df_test.SmartScreen.value_counts()

requireadmin     3413570
existsnotset      601121
existsnotsetf     163164
warn              125926
prompt             28889
block              21242
on                   939
Name: SmartScreen, dtype: int64

df.Platform.unique()

[windows10, windows7, windows8, windows2016]
Categories (4, object): [windows10, windows7, windows8, windows2016]

df[df.SmartScreen.isin(['on'])]['Platform'].value_counts()

windows10      876
windows7         2
windows8         0
windows2016      0
Name: Platform, dtype: int64

df[df.Platform=='windows8']['SmartScreen'].value_counts()

requireadmin    158471
existsnotset     33345
off               1605
prompt            1074
Name: SmartScreen, dtype: int64

df[df.Platform=='windows7']['SmartScreen'].value_counts()

requireadmin    89306
existsnotset     4416
off               159
on                  2
warn                1
Name: SmartScreen, dtype: int64

df[df.Platform=='windows10']['SmartScreen'].value_counts()

requireadmin    7242077
existsnotset    1008045
off              177058
warn             135483
prompt            31908
block             22533
on                  876
Name: SmartScreen, dtype: int64

df[df.Platform=='windows2016']['SmartScreen'].value_counts()

off             9085
requireadmin    3351
prompt          1552
existsnotset     377
Name: SmartScreen, dtype: int64

df[df.SmartScreen=='prompt']['Platform'].value_counts()

windows10      31908
windows2016     1552
windows8        1074
windows7           0
Name: Platform, dtype: int64

df.OsBuild.nunique()

76

df_test.OsBuild.nunique()

78

df.OsBuild.value_counts().head()

17134    3915214
16299    2503359
15063     780211
14393     730782
10586     411599
Name: OsBuild, dtype: int64

df[df.OsVer.str.startswith('6.1.')]['Platform'].value_counts()

windows7       93884
windows8           0
windows2016        0
windows10          0
Name: Platform, dtype: int64

df[df.OsVer.str.startswith('6.3.')]['Platform'].value_counts()

windows8       194495
windows7            0
windows2016         0
windows10           0
Name: Platform, dtype: int64

df[df.OsVer.str.startswith('10.0.0.0')]['Platform'].value_counts()

windows10      8617439
windows2016      14365
windows8             0
windows7             0
Name: Platform, dtype: int64

categorical_columns=['ProductName', 'EngineVersion', 'AppVersion','AvSigVersion','RtpStateBitfield','AVProductStatesIdentifier','CountryIdentifier', 'CityIdentifier','OrganizationIdentifier', 'GeoNameIdentifier','LocaleEnglishNameIdentifier', 'Platform', 'Processor', 'OsVer','OsBuild', 'OsSuite', 'OsPlatformSubRelease', 'OsBuildLab','SkuEdition','IeVerIdentifier', 'SmartScreen','UacLuaenable','Census_MDC2FormFactor','Census_DeviceFamily', 'Census_OEMNameIdentifier','Census_OEMModelIdentifier','Census_ProcessorManufacturerIdentifier','Census_ProcessorModelIdentifier','Census_PrimaryDiskTypeName','Census_ChassisTypeName','Census_PowerPlatformRoleName','Census_OSVersion', 'Census_OSArchitecture', 'Census_OSBranch','Census_OSBuildNumber', 'Census_OSBuildRevision', 'Census_OSEdition','Census_OSSkuName', 'Census_OSInstallTypeName', 'Census_OSInstallLanguageIdentifier', 'Census_OSUILocaleIdentifier','Census_OSWUAutoUpdateOptionsName','Census_GenuineStateName', 'Census_ActivationChannel','Census_FlightRing','Census_FirmwareManufacturerIdentifier','Census_FirmwareVersionIdentifier','Wdft_RegionIdentifier']

numerical_columns=['Census_ProcessorCoreCount','Census_PrimaryDiskTotalCapacity','Census_SystemVolumeTotalCapacity','Census_TotalPhysicalRAM','Census_InternalPrimaryDiagonalDisplaySizeInInches','Census_InternalPrimaryDisplayResolutionHorizontal','Census_InternalPrimaryDisplayResolutionVertical','Census_InternalBatteryNumberOfCharges']

binary_columns=list(set(df.columns)-set(categorical_columns)-set(numerical_columns)-set(['MachineIdentifier','HasDetections']))

df[categorical_columns]=df[categorical_columns].astype('category')

#Creating a dictionary for missing values in 
column_value_replacements={}
for col in df.columns[1:]:
    if col in df.select_dtypes(include=['category','object']).columns:
        column_value_replacements[col]=statistics.mode(df[df[col].notna()][col])
    else:
        column_value_replacements[col]=np.nanmedian(df[col])

country_city={}
for val in df['CountryIdentifier'].unique():
    country_city[val]=statistics.mode(df[df.CountryIdentifier==val]['CityIdentifier'])

%%time
df['CityIdentifier']=[country_city[y] if pd.isnull(x) else x for x,y in zip(df.CityIdentifier,df.CountryIdentifier)]

Wall time: 9 s

%%time
df_test['CityIdentifier']=[country_city[y] if pd.isnull(x) else x for x,y in zip(df_test.CityIdentifier,df_test.CountryIdentifier)]

Wall time: 7.34 s

df_test[categorical_columns]=df_test[categorical_columns].astype('category')

%%time
for col in df_test.columns[df_test.isnull().any()]:
    df_test[col].fillna(column_value_replacements[col],inplace=True)

Wall time: 17.1 s

%%time
for col in df.columns[df.isnull().any()]:
    df[col].fillna(column_value_replacements[col],inplace=True)

Wall time: 19.5 s

def reduce_memory(df):
    initial=df.memory_usage(deep=True).sum()
    print("Changing Datatypes please wait.......")
    for col in df.select_dtypes(include=['int64','int32','float64','float32','int16']).columns:
        try:
            if df[col].dtype in ['int8','int16','int32','int64']:
                if (np.max(df[col])<np.iinfo('int8').max)&(np.min(df[col])>np.iinfo('int8').min):
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'int8')
                    df[col]=df[col].astype('int8')
                elif (np.max(df[col])<np.iinfo('int16').max)&(np.min(df[col])>np.iinfo('int16').min):
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'int16')
                    df[col]=df[col].astype('int16')
                elif (np.max(df[col])<np.iinfo('int32').max)&(np.min(df[col])>np.iinfo('int32').min):
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'int32')
                    df[col]=df[col].astype('int32')
                else:
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'int64')
                    df[col]=df[col].astype('int64')
            else:
                if (np.max(df[col])<np.finfo('float16').max)&(np.min(df[col])>np.finfo('float16').min):
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'float16')
                    df[col]=df[col].astype('float16')
                elif (np.max(df[col])<np.finfo('float32').max)&(np.min(df[col])>np.finfo('float32').min):
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'float32')
                    df[col]=df[col].astype('float32')
                else:
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'float64')
                    df[col]=df[col].astype('float64')
        except:
            print("Exception for column ",col)
            continue
    final=df.memory_usage(deep=True).sum()
    print("Datatypes updated and memory usage is reduced by :",initial-final)
    return df

def my_plots(df,col):
    osver_10=df[col].value_counts().reset_index()['index'].head(10).tolist()

    #df[df.HasDetections==1][col].value_counts()

    t1=df[df.HasDetections==1][col].value_counts().reset_index()

    firmware_affected=t1[t1['index'].isin(osver_10)][col].tolist()

    t2=df[df.HasDetections==0][col].value_counts().reset_index()
    firmware_unaffected=t2[t2['index'].isin(osver_10)][col].tolist()

    index=np.arange(len(osver_10))

    #engine_affected

    width=0.3

    plt.figure(figsize=(12,6))
    plt.barh(index,firmware_affected,width,color='r',label='Affected')
    plt.barh(index+width,firmware_unaffected,width,color='g',label='Unaffected')
    plt.ylabel(col)
    plt.xlabel('Count')
    plt.yticks(index+width/2,osver_10,rotation=0)
    plt.legend(loc='best')
    plt.show()

def year_month(df):
    df['OsBuildLab']=df.OsBuildLab.astype('str')
    temp=df.OsBuildLab.str.split(".",expand=True)

    temp=temp[4].str.split("-",expand=True)

    time_train=pd.DataFrame()

    time_train['Year']=temp[0].astype('int')//10000

    time_train['Month']=(temp[0].astype('int')%10000)//100

    time_train['Day']=(temp[0].astype('int')%10000)%100

    #time_train['Year'].value_counts()

    #time_train['Month'].value_counts()

    #time_train['Day'].value_counts()
    #All Day id greater than 31 belong to November and December 2017.
    #temp[temp[0].str.endswith('53')]

    #temp[temp[0].str.endswith('57')]

    #temp[temp[0].str.endswith('77')]

    #temp[temp[0].str.endswith('32')]

    #temp[temp[0].str.endswith('78')]

    df['Year']=time_train['Year']
    df['Month']=time_train['Month']
    return df

%%time
df=year_month(df)

Wall time: 1min 45s

df_test.loc[6529507,'OsBuildLab']=df_test.loc[6529507,'OsBuildLab'].replace('*','.')

#temp=df_test.OsBuildLab.str.split(".",expand=True)

#temp=temp[4].str.split("-",expand=True)

%%time
df_test=year_month(df_test)

Wall time: 1min 47s

#df_train=df[~df.isnull().any(axis=1)]

sns.countplot(data=df,x='Year',hue='HasDetections')
plt.show()

sns.countplot(data=df,x='Year')
plt.show()

sns.countplot(data=df_test,x='Year')
plt.show()

sns.countplot(data=df,x='Month',hue='HasDetections')
plt.show()

sns.countplot(data=df,x='Month')
plt.show()

sns.countplot(data=df_test,x='Month')
plt.show()

df[(df.Month!=4)&(df.Year!=18)]['HasDetections'].value_counts()

0    1616458
1    1425774
Name: HasDetections, dtype: int64

#df[df.Year==18]['Census_InternalBatteryNumberOfCharges'].boxplot()

sns.boxplot(data=df[df.Year==15],y='Census_InternalBatteryNumberOfCharges',x='HasDetections')

<matplotlib.axes._subplots.AxesSubplot at 0x1ce7f2dc320>

for val in df.Year.unique():
    sns.countplot(data=df[df.Year==val],x='Month',hue='HasDetections')
    plt.title("Year "+str(val))
    plt.show()

df=pd.get_dummies(data=df,columns=['Month','Year'],dtype='int8')
df_test=pd.get_dummies(data=df_test,columns=['Month','Year'],dtype='int8')

month_columns=df.columns[df.columns.str.startswith('Month')]
year_columns=df.columns[df.columns.str.startswith('Year')]

month_columns

Index(['Month_1', 'Month_2', 'Month_3', 'Month_4', 'Month_5', 'Month_6',
       'Month_7', 'Month_8', 'Month_9', 'Month_10', 'Month_11', 'Month_12'],
      dtype='object')

%%time
#i=1
for year in year_columns:
    for month in month_columns:
        new_col='Interaction_MY'+str(year)+str(month)
        df[new_col]=np.multiply(df[year],df[month])
        #i=i+1

Wall time: 8.2 s

df.head()

%%time
i=1
for year in year_columns:
    for month in month_columns:
        new_col='Interaction_MY'+str(year)+str(month)
        df_test[new_col]=np.multiply(df_test[year],df_test[month])
        i=i+1

Wall time: 7.06 s

#df_test.SmartScreen.replace('of','off',inplace=True)
#df_test.SmartScreen.replace('requiredadmin','requireadmin',inplace=True)

df['Device_PossibleOwnership']=[0 if x=='requireadmin' else 1 for x in df['SmartScreen']]
df_test['Device_PossibleOwnership']=[0 if x=='requireadmin' else 1 for x in df_test['SmartScreen']]

%%time
df=reduce_memory(df=df)

Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 365749684
Wall time: 17.4 s

%%time
df_test=reduce_memory(df=df_test)

Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 267010602
Wall time: 14.6 s

fig,ax=plt.subplots(figsize=(12,7))
sns.boxplot(data=df,x=categorical_columns[0],y=numerical_columns[7],hue='HasDetections',ax=ax)

<matplotlib.axes._subplots.AxesSubplot at 0x2682f7f9eb8>

fig,ax=plt.subplots(figsize=(12,7))
sns.barplot(data=df,x=categorical_columns[0],y=numerical_columns[0],ax=ax)
plt.show()

C:\Users\gandh\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

fig,ax=plt.subplots(figsize=(12,7))
sns.barplot(data=df,x='OsPlatformSubRelease',y=numerical_columns[0],ax=ax)
plt.show()

C:\Users\gandh\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

fig,ax=plt.subplots(figsize=(12,7))
sns.barplot(data=df,x='Census_DeviceFamily',y=numerical_columns[0],ax=ax)
plt.show()

C:\Users\gandh\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

fig,ax=plt.subplots(figsize=(12,7))
sns.violinplot(data=df,x=categorical_columns[0],y=numerical_columns[0],hue='HasDetections')

C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\_methods.py:107: RuntimeWarning: overflow encountered in reduce
  arrmean = umr_sum(arr, axis, dtype, keepdims=True)
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\function_base.py:133: RuntimeWarning: invalid value encountered in multiply
  y *= step
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\function_base.py:142: RuntimeWarning: invalid value encountered in add
  y += start
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\_methods.py:28: RuntimeWarning: invalid value encountered in reduce
  return umr_maximum(a, axis, None, out, keepdims, initial)
C:\Users\gandh\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

<matplotlib.axes._subplots.AxesSubplot at 0x1f8f62342b0>

fig,ax=plt.subplots(figsize=(12,7))
sns.violinplot(data=df,x=categorical_columns[0],y=numerical_columns[4],hue='HasDetections')

C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\_methods.py:107: RuntimeWarning: overflow encountered in reduce
  arrmean = umr_sum(arr, axis, dtype, keepdims=True)
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\function_base.py:133: RuntimeWarning: invalid value encountered in multiply
  y *= step
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\function_base.py:142: RuntimeWarning: invalid value encountered in add
  y += start
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\_methods.py:28: RuntimeWarning: invalid value encountered in reduce
  return umr_maximum(a, axis, None, out, keepdims, initial)
C:\Users\gandh\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

<matplotlib.axes._subplots.AxesSubplot at 0x1f932940ac8>

df[numerical_columns].describe().T

np.min(df.Census_TotalPhysicalRAM)

255.0

df[df.Census_TotalPhysicalRAM>409600].shape

(34, 219)

df[df.Census_TotalPhysicalRAM>509600][['OsPlatformSubRelease','AVProductStatesIdentifier','RtpStateBitfield','Census_DeviceFamily','Wdft_IsGamer','AVProductsEnabled','HasDetections']]

df[categorical_columns].describe().T

df_test.AvSigVersion.nunique()

9357

df.AvSigVersion.nunique()

8531

len(set(df_test.AvSigVersion.unique())-set(df.AvSigVersion.unique()))

1092

fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['HasDetections'],hue=df['OsPlatformSubRelease'],ax=ax)

<matplotlib.axes._subplots.AxesSubplot at 0x1cf08da03c8>

fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['OsPlatformSubRelease'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x24069fd35f8>

fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['Platform'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x23fb17eee10>

fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['Census_ProcessorManufacturerIdentifier'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x240c3c657f0>

fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['RtpStateBitfield'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x24105bcceb8>

fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['IsBeta'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x2405aabe7b8>

fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['AVProductsInstalled'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x240ff48e048>

fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['AVProductsEnabled'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x240a73d8c18>

fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['SkuEdition'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x24126963518>

fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df['SmartScreen'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x23f0e29ecf8>

fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df['Census_MDC2FormFactor'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x2406ee1cdd8>

fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df['Wdft_IsGamer'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x2413092aef0>

fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df['AVProductsInstalled'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x240d22a7518>

fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[df.Wdft_IsGamer==1]['AVProductsInstalled'],hue=df[df.Wdft_IsGamer==1]['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x23ef9ffd0f0>

fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[df.Wdft_IsGamer==1]['AVProductsEnabled'],hue=df[df.Wdft_IsGamer==1]['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x2405cfbf160>

fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[df.EngineVersion.isin(['1.1.15200.1','1.1.15100.1'])]['AVProductsEnabled'],hue=df[df.EngineVersion.isin(['1.1.15200.1','1.1.15100.1'])]['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x2412d318a58>

fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[df.EngineVersion.isin(['1.1.15200.1','1.1.15100.1'])]['Wdft_IsGamer'],hue=df[df.EngineVersion.isin(['1.1.15200.1','1.1.15100.1'])]['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x2409768ea90>

fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[df.AppVersion=='4.18.1807.18075']['Wdft_IsGamer'],hue=df[df.AppVersion=='4.18.1807.18075']['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x23ef26f2278>

fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df['Wdft_IsGamer'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x24093c82cc0>

fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[(df.AVProductsInstalled==1)&(df.Wdft_IsGamer==1)]['ProductName'],hue=df[(df.AVProductsInstalled==1)&(df.Wdft_IsGamer==1)]['HasDetections'],ax=ax,palette={0:'g',1:'r'})

<matplotlib.axes._subplots.AxesSubplot at 0x1fbecdc0390>

#df['AVProductsInstalled_1']=[1 if x==1 else 0 for x in df['AVProductsInstalled']]

#df['Interaction_17']=np.logical_and(np.logical_and(df.AVProductsInstalled_1,df.Wdft_IsGamer),df.ProductName_win8defender.astype('bool')).astype('int8')

my_plots(df,'OsBuild')

my_plots(df,categorical_columns[1])

my_plots(df,categorical_columns[2])

my_plots(df,categorical_columns[3])

my_plots(df,categorical_columns[4])

my_plots(df,categorical_columns[5])

my_plots(df,categorical_columns[6])

my_plots(df,categorical_columns[7])

my_plots(df,categorical_columns[8])

my_plots(df,categorical_columns[9])

my_plots(df,categorical_columns[10])

my_plots(df,categorical_columns[11])

my_plots(df,categorical_columns[12])

my_plots(df,categorical_columns[13])

my_plots(df,categorical_columns[14])

my_plots(df,categorical_columns[15])

my_plots(df,categorical_columns[16])

my_plots(df,categorical_columns[17])

my_plots(df,categorical_columns[18])

my_plots(df,categorical_columns[19])

my_plots(df,categorical_columns[21])

my_plots(df,categorical_columns[22])

my_plots(df,categorical_columns[23])

my_plots(df,categorical_columns[24])

my_plots(df,categorical_columns[25])

my_plots(df,categorical_columns[26])

my_plots(df,categorical_columns[27])

my_plots(df,categorical_columns[28])

my_plots(df,categorical_columns[29])

my_plots(df,categorical_columns[30])

my_plots(df,categorical_columns[31])

my_plots(df,categorical_columns[32])

my_plots(df,categorical_columns[33])

my_plots(df,categorical_columns[34])

my_plots(df,categorical_columns[35])

my_plots(df,categorical_columns[36])

my_plots(df,categorical_columns[37])

my_plots(df,categorical_columns[38])

my_plots(df,categorical_columns[39])

my_plots(df,categorical_columns[40])

my_plots(df,categorical_columns[41])

my_plots(df,categorical_columns[42])

my_plots(df,categorical_columns[43])

my_plots(df,categorical_columns[44])

my_plots(df,categorical_columns[45])

my_plots(df,categorical_columns[46])

my_plots(df,categorical_columns[47])

Frequency Encoding¶

import category_encoders as ce

from tqdm import tqdm
import warnings
import gc
import time
from sklearn.metrics import mean_squared_error
warnings.simplefilter(action='ignore', category=FutureWarning)
def frequency_encoding(df,df_test,variable):
    t = pd.concat([df[variable], df_test[variable]]).value_counts().reset_index()
    t = t.reset_index()
    t.loc[t[variable] == 1, 'level_0'] = np.nan
    t.set_index('index', inplace=True)
    max_label = t['level_0'].max() + 1
    t.fillna(max_label, inplace=True)
    return t.to_dict()['level_0']

%%time
for variable in tqdm(categorical_columns):
    freq_enc_dict = frequency_encoding(df,df_test,variable)
    df[variable] = df[variable].map(lambda x: freq_enc_dict.get(x, np.nan))
    df_test[variable] = df_test[variable].map(lambda x: freq_enc_dict.get(x, np.nan))
    categorical_columns.remove(variable)

 50%|█████████████████████████████████████████                                         | 24/48 [00:43<00:57,  2.38s/it]

Wall time: 43.9 s

%%time
indexer = {}
for col in tqdm(categorical_columns):
    if col == 'MachineIdentifier': continue
    _, indexer[col] = pd.factorize(df[col])
    
for col in tqdm(categorical_columns):
    if col == 'MachineIdentifier': continue
    df[col] = indexer[col].get_indexer(df[col])
    df_test[col] = indexer[col].get_indexer(df_test[col])

100%|██████████████████████████████████████████████████████████████████████████████████| 24/24 [00:09<00:00,  3.53it/s]
100%|██████████████████████████████████████████████████████████████████████████████████| 24/24 [36:39<00:00, 67.87s/it]

Wall time: 36min 48s

%%time
for col in df.columns[1:]:
    if df[col].nunique()==2:
        print(df[col].value_counts())
        print("\n")

0    8920657
1         67
Name: IsBeta, dtype: int64


0    8766103
1     154621
Name: IsSxsPassiveMode, dtype: int64


1    8813414
0     107310
Name: HasTpm, dtype: int64


1.0    8437593
0.0     483131
Name: IsProtected, dtype: int64


0    8920466
1        258
Name: AutoSampleOptIn, dtype: int64


0.0    8916843
1.0       3881
Name: SMode, dtype: int64


1.0    8731622
0.0     189102
Name: Firewall, dtype: int64


0    8232180
1     688544
Name: Census_HasOpticalDiskDrive, dtype: int64


0    8915860
1       4864
Name: Census_IsPortableOperatingSystem, dtype: int64


0.0    8920637
1.0         87
Name: Census_IsFlightsDisabled, dtype: int64


0.0    8919908
1.0        816
Name: Census_ThresholdOptIn, dtype: int64


0    4584979
1    4335745
Name: Census_IsSecureBootEnabled, dtype: int64


0.0    8920723
1.0          1
Name: Census_IsWIMBootEnabled, dtype: int64


0.0    8858046
1.0      62678
Name: Census_IsVirtualDevice, dtype: int64


0    7800821
1    1119903
Name: Census_IsTouchEnabled, dtype: int64


0    8581166
1     339558
Name: Census_IsPenCapable, dtype: int64


0.0    8412643
1.0     508081
Name: Census_IsAlwaysOnAlwaysConnectedCapable, dtype: int64


0.0    6477019
1.0    2443705
Name: Wdft_IsGamer, dtype: int64


0    4462256
1    4458468
Name: HasDetections, dtype: int64


0    8899401
1      21323
Name: Month_1, dtype: int64


0    8872556
1      48168
Name: Month_2, dtype: int64


0    7826917
1    1093807
Name: Month_3, dtype: int64


0    4897093
1    4023631
Name: Month_4, dtype: int64


0    7629648
1    1291076
Name: Month_5, dtype: int64


0    8580966
1     339758
Name: Month_6, dtype: int64


0    8697782
1     222942
Name: Month_7, dtype: int64


0    8766232
1     154492
Name: Month_8, dtype: int64


0    7441694
1    1479030
Name: Month_9, dtype: int64


0    8821495
1      99229
Name: Month_10, dtype: int64


0    8851301
1      69423
Name: Month_11, dtype: int64


0    8842879
1      77845
Name: Month_12, dtype: int64


0    8920567
1        157
Name: Year_9, dtype: int64


0    8920543
1        181
Name: Year_10, dtype: int64


0    8920430
1        294
Name: Year_11, dtype: int64


0    8920684
1         40
Name: Year_12, dtype: int64


0    8920458
1        266
Name: Year_13, dtype: int64


0    8919032
1       1692
Name: Year_14, dtype: int64


0    8829890
1      90834
Name: Year_15, dtype: int64


0    8599707
1     321017
Name: Year_16, dtype: int64


0    6249001
1    2671723
Name: Year_17, dtype: int64


1    5834520
0    3086204
Name: Year_18, dtype: int64


0    8920569
1        155
Name: Interaction_MYYear_9Month_7, dtype: int64


0    8920722
1          2
Name: Interaction_MYYear_9Month_12, dtype: int64


0    8920712
1         12
Name: Interaction_MYYear_10Month_2, dtype: int64


0    8920714
1         10
Name: Interaction_MYYear_10Month_6, dtype: int64


0    8920723
1          1
Name: Interaction_MYYear_10Month_9, dtype: int64


0    8920716
1          8
Name: Interaction_MYYear_10Month_10, dtype: int64


0    8920574
1        150
Name: Interaction_MYYear_10Month_11, dtype: int64


0    8920477
1        247
Name: Interaction_MYYear_11Month_4, dtype: int64


0    8920688
1         36
Name: Interaction_MYYear_11Month_6, dtype: int64


0    8920721
1          3
Name: Interaction_MYYear_11Month_10, dtype: int64


0    8920716
1          8
Name: Interaction_MYYear_11Month_11, dtype: int64


0    8920703
1         21
Name: Interaction_MYYear_12Month_3, dtype: int64


0    8920720
1          4
Name: Interaction_MYYear_12Month_4, dtype: int64


0    8920720
1          4
Name: Interaction_MYYear_12Month_5, dtype: int64


0    8920713
1         11
Name: Interaction_MYYear_12Month_8, dtype: int64


0    8920719
1          5
Name: Interaction_MYYear_13Month_1, dtype: int64


0    8920671
1         53
Name: Interaction_MYYear_13Month_3, dtype: int64


0    8920721
1          3
Name: Interaction_MYYear_13Month_5, dtype: int64


0    8920720
1          4
Name: Interaction_MYYear_13Month_7, dtype: int64


0    8920539
1        185
Name: Interaction_MYYear_13Month_8, dtype: int64


0    8920723
1          1
Name: Interaction_MYYear_13Month_9, dtype: int64


0    8920709
1         15
Name: Interaction_MYYear_13Month_10, dtype: int64


0    8920704
1         20
Name: Interaction_MYYear_14Month_2, dtype: int64


0    8920075
1        649
Name: Interaction_MYYear_14Month_3, dtype: int64


0    8920381
1        343
Name: Interaction_MYYear_14Month_7, dtype: int64


0    8920615
1        109
Name: Interaction_MYYear_14Month_8, dtype: int64


0    8920179
1        545
Name: Interaction_MYYear_14Month_10, dtype: int64


0    8920698
1         26
Name: Interaction_MYYear_14Month_12, dtype: int64


0    8920509
1        215
Name: Interaction_MYYear_15Month_1, dtype: int64


0    8920691
1         33
Name: Interaction_MYYear_15Month_2, dtype: int64


0    8920168
1        556
Name: Interaction_MYYear_15Month_3, dtype: int64


0    8920688
1         36
Name: Interaction_MYYear_15Month_4, dtype: int64


0    8920573
1        151
Name: Interaction_MYYear_15Month_5, dtype: int64


0    8885886
1      34838
Name: Interaction_MYYear_15Month_7, dtype: int64


0    8914201
1       6523
Name: Interaction_MYYear_15Month_8, dtype: int64


0    8919121
1       1603
Name: Interaction_MYYear_15Month_9, dtype: int64


0    8888974
1      31750
Name: Interaction_MYYear_15Month_10, dtype: int64


0    8905750
1      14974
Name: Interaction_MYYear_15Month_11, dtype: int64


0    8920569
1        155
Name: Interaction_MYYear_15Month_12, dtype: int64


0    8906060
1      14664
Name: Interaction_MYYear_16Month_1, dtype: int64


0    8895566
1      25158
Name: Interaction_MYYear_16Month_2, dtype: int64


0    8912207
1       8517
Name: Interaction_MYYear_16Month_3, dtype: int64


0    8906477
1      14247
Name: Interaction_MYYear_16Month_4, dtype: int64


0    8907956
1      12768
Name: Interaction_MYYear_16Month_5, dtype: int64


0    8905557
1      15167
Name: Interaction_MYYear_16Month_6, dtype: int64


0    8857429
1      63295
Name: Interaction_MYYear_16Month_7, dtype: int64


0    8902084
1      18640
Name: Interaction_MYYear_16Month_8, dtype: int64


0    8898163
1      22561
Name: Interaction_MYYear_16Month_9, dtype: int64


0    8870372
1      50352
Name: Interaction_MYYear_16Month_10, dtype: int64


0    8895744
1      24980
Name: Interaction_MYYear_16Month_11, dtype: int64


0    8870056
1      50668
Name: Interaction_MYYear_16Month_12, dtype: int64


0    8919999
1        725
Name: Interaction_MYYear_17Month_1, dtype: int64


0    8919326
1       1398
Name: Interaction_MYYear_17Month_2, dtype: int64


0    8096779
1     823945
Name: Interaction_MYYear_17Month_3, dtype: int64


0    8891286
1      29438
Name: Interaction_MYYear_17Month_4, dtype: int64


0    8919438
1       1286
Name: Interaction_MYYear_17Month_5, dtype: int64


0    8690223
1     230501
Name: Interaction_MYYear_17Month_6, dtype: int64


0    8860746
1      59978
Name: Interaction_MYYear_17Month_7, dtype: int64


0    8919186
1       1538
Name: Interaction_MYYear_17Month_8, dtype: int64


0    7470671
1    1450053
Name: Interaction_MYYear_17Month_9, dtype: int64


0    8904168
1      16556
Name: Interaction_MYYear_17Month_10, dtype: int64


0    8891413
1      29311
Name: Interaction_MYYear_17Month_11, dtype: int64


0    8893730
1      26994
Name: Interaction_MYYear_17Month_12, dtype: int64


0    8915010
1       5714
Name: Interaction_MYYear_18Month_1, dtype: int64


0    8899177
1      21547
Name: Interaction_MYYear_18Month_2, dtype: int64


0    8660658
1     260066
Name: Interaction_MYYear_18Month_3, dtype: int64


0    4941065
1    3979659
Name: Interaction_MYYear_18Month_4, dtype: int64


0    7643860
1    1276864
Name: Interaction_MYYear_18Month_5, dtype: int64


0    8826680
1      94044
Name: Interaction_MYYear_18Month_6, dtype: int64


0    8856395
1      64329
Name: Interaction_MYYear_18Month_7, dtype: int64


0    8793238
1     127486
Name: Interaction_MYYear_18Month_8, dtype: int64


0    8915913
1       4811
Name: Interaction_MYYear_18Month_9, dtype: int64


0    7493205
1    1427519
Name: Device_PossibleOwnership, dtype: int64


Wall time: 41.7 s

df.drop(columns=['Interaction_MYYear_15Month_12','Interaction_MYYear_15Month_5','Interaction_MYYear_15Month_4','Interaction_MYYear_15Month_2','Interaction_MYYear_14Month_12','Interaction_MYYear_14Month_8','Interaction_MYYear_14Month_2','Interaction_MYYear_13Month_10','Interaction_MYYear_13Month_9','Interaction_MYYear_13Month_8','Interaction_MYYear_13Month_7','Interaction_MYYear_13Month_5','Interaction_MYYear_13Month_3','Interaction_MYYear_13Month_1','Interaction_MYYear_12Month_8','Interaction_MYYear_12Month_5','Interaction_MYYear_12Month_4','Interaction_MYYear_12Month_3','Interaction_MYYear_11Month_11','Interaction_MYYear_11Month_10','Interaction_MYYear_11Month_6','Interaction_MYYear_11Month_4','Interaction_MYYear_10Month_11','Interaction_MYYear_10Month_10','Interaction_MYYear_10Month_9','Interaction_MYYear_10Month_6','Year_9','Year_10','Year_11','Year_12','Year_13','Interaction_MYYear_9Month_7','Interaction_MYYear_9Month_12','Interaction_MYYear_10Month_2'],inplace=True)
df_test.drop(columns=['Interaction_MYYear_15Month_12','Interaction_MYYear_15Month_5','Interaction_MYYear_15Month_4','Interaction_MYYear_15Month_2','Interaction_MYYear_14Month_12','Interaction_MYYear_14Month_8','Interaction_MYYear_14Month_2','Interaction_MYYear_13Month_10','Interaction_MYYear_13Month_9','Interaction_MYYear_13Month_8','Interaction_MYYear_13Month_7','Interaction_MYYear_13Month_5','Interaction_MYYear_13Month_3','Interaction_MYYear_13Month_1','Interaction_MYYear_12Month_8','Interaction_MYYear_12Month_5','Interaction_MYYear_12Month_4','Interaction_MYYear_12Month_3','Interaction_MYYear_11Month_11','Interaction_MYYear_11Month_10','Interaction_MYYear_11Month_6','Interaction_MYYear_11Month_4','Interaction_MYYear_10Month_11','Interaction_MYYear_10Month_10','Interaction_MYYear_10Month_9','Interaction_MYYear_10Month_6','Year_9','Year_10','Year_11','Year_12','Year_13','Interaction_MYYear_9Month_7','Interaction_MYYear_9Month_12','Interaction_MYYear_10Month_2'],inplace=True)

df.shape

(8920724, 187)

df_test.shape

(7853253, 186)

%%time
#df.drop(columns=list(df.columns[df.columns.str.endswith('encode')]),inplace=True)
#df_test.drop(columns=list(df_test.columns[df_test.columns.str.endswith('encode')]),inplace=True)

Wall time: 0 ns

%%time
df=reduce_memory(df)
df_test=reduce_memory(df_test)

Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 1793065524
Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 1625623371
Wall time: 53.1 s

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 8920724 entries, 0 to 8921482
Columns: 187 entries, MachineIdentifier to Device_PossibleOwnership
dtypes: category(17), float16(23), float32(4), int16(9), int32(2), int8(132)
memory usage: 2.5 GB

df_test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7853253 entries, 0 to 7853252
Columns: 186 entries, MachineIdentifier to Device_PossibleOwnership
dtypes: category(16), float16(24), float32(4), int16(9), int32(2), int8(131)
memory usage: 2.1 GB

%%time
s=df['AVProductsInstalled']-df['AVProductsEnabled']
r=df_test['AVProductsInstalled']-df_test['AVProductsEnabled']

df['AV_highrisk']=[1 if (x==0)&(y==0) else 0 for x,y in zip(df.AVProductsInstalled,s)]
df_test['AV_highrisk']=[1 if (x==0)&(y==0) else 0 for x,y in zip(df_test.AVProductsInstalled,r)]



df['AV_mediumrisk']=[1 if (x==1)&(y==0) else 0 for x,y in zip(df.AVProductsInstalled,s)]
df_test['AV_mediumrisk']=[1 if (x==1)&(y==0) else 0 for x,y in zip(df_test.AVProductsInstalled,r)]

df['AV_lowrisk']=[1 if (x>1)&(y>1) else 0 for x,y in zip(df.AVProductsInstalled,s)]
df_test['AV_lowrisk']=[1 if (x==0)&(y==0) else 0 for x,y in zip(df_test.AVProductsInstalled,r)]

Wall time: 22.5 s

df.columns[df.isnull().any()]

Index([], dtype='object')

df.shape

(8920724, 190)

df_test.shape

(7853253, 189)

df.memory_usage(deep=True).sum()

3573865091

df_test.memory_usage(deep=True).sum()

3115615234

df[numerical_columns].corr()

max(df.Census_InternalPrimaryDisplayResolutionVertical)

8640.0

max(df.Census_InternalPrimaryDisplayResolutionHorizontal)

12288.0

df[df.Census_InternalPrimaryDisplayResolutionHorizontal==12288.0][numerical_columns]

display_horizontal=np.median(df.Census_InternalPrimaryDisplayResolutionHorizontal)

display_vertical=np.median(df.Census_InternalPrimaryDisplayResolutionVertical)

df['Census_InternalPrimaryDisplayResolutionHorizontal']=[display_horizontal if x>=10000.0 else x for x in df['Census_InternalPrimaryDisplayResolutionHorizontal']]
df['Census_InternalPrimaryDisplayResolutionVertical']=[display_vertical if x>=6000 else x for x in df['Census_InternalPrimaryDisplayResolutionVertical']]
df_test['Census_InternalPrimaryDisplayResolutionHorizontal']=[display_horizontal if x>=10000.0 else x for x in df_test['Census_InternalPrimaryDisplayResolutionHorizontal']]
df_test['Census_InternalPrimaryDisplayResolutionVertical']=[display_vertical if x>=6000 else x for x in df_test['Census_InternalPrimaryDisplayResolutionVertical']]

df['Interaction_01']=df['Census_TotalPhysicalRAM']/df['Census_ProcessorCoreCount']
df_test['Interaction_01']=df_test['Census_TotalPhysicalRAM']/df_test['Census_ProcessorCoreCount']
df['Interaction_02']=abs(df.Census_PrimaryDiskTotalCapacity- df.Census_SystemVolumeTotalCapacity)
df_test['Interaction_02']=abs(df_test.Census_PrimaryDiskTotalCapacity- df_test.Census_SystemVolumeTotalCapacity)
df['Interaction_03']=np.multiply(df.Census_InternalPrimaryDisplayResolutionHorizontal,df.Census_InternalPrimaryDisplayResolutionVertical)
df_test['Interaction_03']=np.multiply(df_test.Census_InternalPrimaryDisplayResolutionHorizontal,df_test.Census_InternalPrimaryDisplayResolutionVertical)

df=reduce_memory(df)

Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 330066788

df_test=reduce_memory(df_test)

Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 290570361

np.min(df.Census_InternalPrimaryDisplayResolutionHorizontal)

-1.0

np.min(df.Census_InternalPrimaryDisplayResolutionVertical)

-1.0

df[df.Census_InternalPrimaryDisplayResolutionHorizontal==-1][numerical_columns]

df['Census_InternalPrimaryDisplayResolutionHorizontal']=[display_horizontal if x==-1 else x for x in df['Census_InternalPrimaryDisplayResolutionHorizontal']]
df['Census_InternalPrimaryDisplayResolutionVertical']=[display_vertical if x==-1 else x for x in df['Census_InternalPrimaryDisplayResolutionVertical']]
df_test['Census_InternalPrimaryDisplayResolutionHorizontal']=[display_horizontal if x==-1 else x for x in df_test['Census_InternalPrimaryDisplayResolutionHorizontal']]
df_test['Census_InternalPrimaryDisplayResolutionVertical']=[display_vertical if x==-1 else x for x in df_test['Census_InternalPrimaryDisplayResolutionVertical']]

df.Census_InternalBatteryNumberOfCharges.describe()

count    8.920724e+06
mean     1.089876e+09
std      1.925254e+09
min      0.000000e+00
25%      0.000000e+00
50%      0.000000e+00
75%      4.294967e+09
max      4.294967e+09
Name: Census_InternalBatteryNumberOfCharges, dtype: float64

df.Census_InternalBatteryNumberOfCharges.value_counts().head()

0.000000e+00    5321817
4.294967e+09    2263693
1.000000e+00      53810
2.000000e+00      28128
1.600000e+01      27348
Name: Census_InternalBatteryNumberOfCharges, dtype: int64

df['PPI']=np.sqrt(np.power(df.Census_InternalPrimaryDisplayResolutionHorizontal,2)+np.power(df.Census_InternalPrimaryDisplayResolutionVertical,2))/df.Census_InternalPrimaryDiagonalDisplaySizeInInches

df_test['PPI']=np.sqrt(np.power(df_test.Census_InternalPrimaryDisplayResolutionHorizontal,2)+np.power(df_test.Census_InternalPrimaryDisplayResolutionVertical,2))/df_test.Census_InternalPrimaryDiagonalDisplaySizeInInches

df=reduce_memory(df)

Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 160573032

df_test=reduce_memory(df_test)

Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 141358554

print("Memory Usage of training set in Bytes : ",df.memory_usage(deep=True).sum())
print("Memory Usage of test set in Bytes : ",df_test.memory_usage(deep=True).sum())

Memory Usage of training set in Bytes :  3511420023
Memory Usage of test set in Bytes :  3060642463

set(df_test.EngineVersion.unique())-set(df.EngineVersion.unique())

{-1}

%%time
different_columns=[]
for i in range(1,len(df_test.columns)):
    if df.columns[i] in ['MachineIdentifier','HasDetections']:
        continue
    elif len(set(df_test[df.columns[i]].unique())-set(df[df.columns[i]].unique()))==0:
        print(df.columns[i])
        continue
    else:
        different_columns.append(df.columns[i])

ProductName
IsBeta
IsSxsPassiveMode
AVProductsInstalled
AVProductsEnabled
HasTpm
CountryIdentifier
LocaleEnglishNameIdentifier
Platform
Processor
OsSuite
OsPlatformSubRelease
SkuEdition
IsProtected
AutoSampleOptIn
SMode
Firewall
Census_DeviceFamily
Census_ProcessorManufacturerIdentifier
Census_PrimaryDiskTypeName
Census_HasOpticalDiskDrive
Census_PowerPlatformRoleName
Census_OSArchitecture
Census_OSInstallTypeName
Census_OSInstallLanguageIdentifier
Census_OSWUAutoUpdateOptionsName
Census_IsPortableOperatingSystem
Census_GenuineStateName
Census_ActivationChannel
Census_IsFlightsDisabled
Census_ThresholdOptIn
Census_IsSecureBootEnabled
Census_IsWIMBootEnabled
Census_IsVirtualDevice
Census_IsTouchEnabled
Census_IsPenCapable
Census_IsAlwaysOnAlwaysConnectedCapable
Wdft_IsGamer
Wdft_RegionIdentifier
Month_1
Month_2
Month_3
Month_4
Month_5
Month_6
Month_7
Month_8
Month_9
Month_10
Month_11
Month_12
Year_14
Year_15
Year_16
Year_17
Year_18
Interaction_MYYear_9Month_1
Interaction_MYYear_9Month_2
Interaction_MYYear_9Month_3
Interaction_MYYear_9Month_4
Interaction_MYYear_9Month_5
Interaction_MYYear_9Month_6
Interaction_MYYear_9Month_9
Interaction_MYYear_9Month_10
Interaction_MYYear_9Month_11
Interaction_MYYear_10Month_1
Interaction_MYYear_10Month_3
Interaction_MYYear_10Month_4
Interaction_MYYear_10Month_5
Interaction_MYYear_10Month_7
Interaction_MYYear_10Month_8
Interaction_MYYear_10Month_12
Interaction_MYYear_11Month_1
Interaction_MYYear_11Month_2
Interaction_MYYear_11Month_3
Interaction_MYYear_11Month_5
Interaction_MYYear_11Month_7
Interaction_MYYear_11Month_8
Interaction_MYYear_11Month_9
Interaction_MYYear_11Month_12
Interaction_MYYear_12Month_1
Interaction_MYYear_12Month_6
Interaction_MYYear_12Month_7
Interaction_MYYear_12Month_9
Interaction_MYYear_12Month_10
Interaction_MYYear_12Month_11
Interaction_MYYear_12Month_12
Interaction_MYYear_13Month_2
Interaction_MYYear_13Month_4
Interaction_MYYear_13Month_6
Interaction_MYYear_13Month_11
Interaction_MYYear_13Month_12
Interaction_MYYear_14Month_1
Interaction_MYYear_14Month_3
Interaction_MYYear_14Month_5
Interaction_MYYear_14Month_6
Interaction_MYYear_14Month_7
Interaction_MYYear_14Month_9
Interaction_MYYear_14Month_10
Interaction_MYYear_14Month_11
Interaction_MYYear_15Month_1
Interaction_MYYear_15Month_3
Interaction_MYYear_15Month_6
Interaction_MYYear_15Month_7
Interaction_MYYear_15Month_8
Interaction_MYYear_15Month_9
Interaction_MYYear_15Month_10
Interaction_MYYear_15Month_11
Interaction_MYYear_16Month_1
Interaction_MYYear_16Month_2
Interaction_MYYear_16Month_3
Interaction_MYYear_16Month_4
Interaction_MYYear_16Month_5
Interaction_MYYear_16Month_6
Interaction_MYYear_16Month_7
Interaction_MYYear_16Month_8
Interaction_MYYear_16Month_9
Interaction_MYYear_16Month_10
Interaction_MYYear_16Month_11
Interaction_MYYear_16Month_12
Interaction_MYYear_17Month_1
Interaction_MYYear_17Month_2
Interaction_MYYear_17Month_3
Interaction_MYYear_17Month_4
Interaction_MYYear_17Month_5
Interaction_MYYear_17Month_6
Interaction_MYYear_17Month_7
Interaction_MYYear_17Month_8
Interaction_MYYear_17Month_9
Interaction_MYYear_17Month_10
Interaction_MYYear_17Month_11
Interaction_MYYear_17Month_12
Interaction_MYYear_18Month_1
Interaction_MYYear_18Month_2
Interaction_MYYear_18Month_3
Interaction_MYYear_18Month_4
Interaction_MYYear_18Month_5
Interaction_MYYear_18Month_6
Interaction_MYYear_18Month_7
Interaction_MYYear_18Month_8
Interaction_MYYear_18Month_9
Interaction_MYYear_18Month_12
Device_PossibleOwnership
AV_highrisk
AV_mediumrisk
AV_lowrisk
Wall time: 39.7 s

for col in different_columns:
    print("Number of Missing Values in Test set for "+str(col)+" : ",len(set(df_test[col].unique())-set(df[col].unique())))
    print("Number of Categories : ",df[col].nunique())
    print("Number of rows with missing categories in test data , ",df_test[df_test[col].isin(list(set(df_test[col].unique())-set(df[col].unique())))].shape[0])
    print("\n")

Number of Missing Values in Test set for EngineVersion :  1
Number of Categories :  70
Number of rows with missing categories in test data ,  3619770


Number of Missing Values in Test set for AppVersion :  10
Number of Categories :  107
Number of rows with missing categories in test data ,  2306148


Number of Missing Values in Test set for AvSigVersion :  1
Number of Categories :  8531
Number of rows with missing categories in test data ,  6598309


Number of Missing Values in Test set for RtpStateBitfield :  1
Number of Categories :  7
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for AVProductStatesIdentifier :  1
Number of Categories :  28969
Number of rows with missing categories in test data ,  16886


Number of Missing Values in Test set for CityIdentifier :  1
Number of Categories :  107366
Number of rows with missing categories in test data ,  52481


Number of Missing Values in Test set for OrganizationIdentifier :  1
Number of Categories :  49
Number of rows with missing categories in test data ,  7


Number of Missing Values in Test set for GeoNameIdentifier :  1
Number of Categories :  292
Number of rows with missing categories in test data ,  4


Number of Missing Values in Test set for OsVer :  1
Number of Categories :  58
Number of rows with missing categories in test data ,  13


Number of Missing Values in Test set for OsBuild :  20
Number of Categories :  61
Number of rows with missing categories in test data ,  7350


Number of Missing Values in Test set for OsBuildLab :  1
Number of Categories :  663
Number of rows with missing categories in test data ,  95381


Number of Missing Values in Test set for IeVerIdentifier :  1
Number of Categories :  303
Number of rows with missing categories in test data ,  27338


Number of Missing Values in Test set for SmartScreen :  1
Number of Categories :  7
Number of rows with missing categories in test data ,  163164


Number of Missing Values in Test set for UacLuaenable :  1
Number of Categories :  11
Number of rows with missing categories in test data ,  2


Number of Missing Values in Test set for Census_MDC2FormFactor :  1
Number of Categories :  13
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for Census_OEMNameIdentifier :  48
Number of Categories :  2137
Number of rows with missing categories in test data ,  147


Number of Missing Values in Test set for Census_OEMModelIdentifier :  1
Number of Categories :  175347
Number of rows with missing categories in test data ,  80321


Number of Missing Values in Test set for Census_ProcessorCoreCount :  4
Number of Categories :  45
Number of rows with missing categories in test data ,  4


Number of Missing Values in Test set for Census_ProcessorModelIdentifier :  1
Number of Categories :  2583
Number of rows with missing categories in test data ,  426


Number of Missing Values in Test set for Census_PrimaryDiskTotalCapacity :  3062
Number of Categories :  5734
Number of rows with missing categories in test data ,  3592


Number of Missing Values in Test set for Census_SystemVolumeTotalCapacity :  99275
Number of Categories :  536839
Number of rows with missing categories in test data ,  139515


Number of Missing Values in Test set for Census_TotalPhysicalRAM :  1830
Number of Categories :  3446
Number of rows with missing categories in test data ,  2363


Number of Missing Values in Test set for Census_ChassisTypeName :  1
Number of Categories :  52
Number of rows with missing categories in test data ,  6


Number of Missing Values in Test set for Census_InternalPrimaryDiagonalDisplaySizeInInches :  84
Number of Categories :  785
Number of rows with missing categories in test data ,  116


Number of Missing Values in Test set for Census_InternalPrimaryDisplayResolutionHorizontal :  386
Number of Categories :  2046
Number of rows with missing categories in test data ,  641


Number of Missing Values in Test set for Census_InternalPrimaryDisplayResolutionVertical :  233
Number of Categories :  1549
Number of rows with missing categories in test data ,  408


Number of Missing Values in Test set for Census_InternalBatteryNumberOfCharges :  11749
Number of Categories :  41087
Number of rows with missing categories in test data ,  17043


Number of Missing Values in Test set for Census_OSVersion :  1
Number of Categories :  469
Number of rows with missing categories in test data ,  2741713


Number of Missing Values in Test set for Census_OSBranch :  1
Number of Categories :  32
Number of rows with missing categories in test data ,  14


Number of Missing Values in Test set for Census_OSBuildNumber :  24
Number of Categories :  130
Number of rows with missing categories in test data ,  7783


Number of Missing Values in Test set for Census_OSBuildRevision :  1
Number of Categories :  285
Number of rows with missing categories in test data ,  2599915


Number of Missing Values in Test set for Census_OSEdition :  1
Number of Categories :  31
Number of rows with missing categories in test data ,  2


Number of Missing Values in Test set for Census_OSSkuName :  1
Number of Categories :  30
Number of rows with missing categories in test data ,  2


Number of Missing Values in Test set for Census_OSUILocaleIdentifier :  1
Number of Categories :  143
Number of rows with missing categories in test data ,  2


Number of Missing Values in Test set for Census_FlightRing :  1
Number of Categories :  10
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for Census_FirmwareManufacturerIdentifier :  1
Number of Categories :  712
Number of rows with missing categories in test data ,  309


Number of Missing Values in Test set for Census_FirmwareVersionIdentifier :  1
Number of Categories :  6569
Number of rows with missing categories in test data ,  1688


Number of Missing Values in Test set for Interaction_MYYear_9Month_8 :  1
Number of Categories :  1
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for Interaction_MYYear_12Month_2 :  1
Number of Categories :  1
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for Interaction_MYYear_14Month_4 :  1
Number of Categories :  1
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for Interaction_MYYear_18Month_10 :  1
Number of Categories :  1
Number of rows with missing categories in test data ,  15364


Number of Missing Values in Test set for Interaction_MYYear_18Month_11 :  1
Number of Categories :  1
Number of rows with missing categories in test data ,  2025


Number of Missing Values in Test set for Interaction_01 :  2593
Number of Categories :  4465
Number of rows with missing categories in test data ,  3169


Number of Missing Values in Test set for Interaction_02 :  140338
Number of Categories :  565079
Number of rows with missing categories in test data ,  200917


Number of Missing Values in Test set for Interaction_03 :  7805
Number of Categories :  9615
Number of rows with missing categories in test data ,  8530

df.drop(columns=numerical_columns,inplace=True)
df_test.drop(columns=numerical_columns,inplace=True)

df.head()

df_test.head()

df.columns[df.isnull().any()]

Index([], dtype='object')

df.drop(columns=['AVProductsEnabled','AVProductsInstalled'],inplace=True)
df_test.drop(columns=['AVProductsEnabled','AVProductsInstalled'],inplace=True)

#df,df_test=frequency_encode(df=df,df_test=df_test,col='Year')
#df,df_test=frequency_encode(df=df,df_test=df_test,col='Month')

df.shape

(8920724, 184)

df_test.shape

(7853253, 183)

for col in year_columns:
    if col in df.columns:
        df.drop(columns=[col],inplace=True)
        df_test.drop(columns=[col],inplace=True)
    else:
        continue

for col in month_columns:
    if col in df.columns:
        df.drop(columns=[col],inplace=True)
        df_test.drop(columns=[col],inplace=True)
    else:
        continue

set(df.columns)-set(df_test.columns)

{'HasDetections'}

%%time
for col in df.columns[1:]:
    if col in ['MachineIdentifier','HasDetections']:
        continue
    elif df[col].nunique()==1:
        print(col)
        df.drop(columns=[col],inplace=True)
        df_test.drop(columns=[col],inplace=True)   
    else:
        continue

Interaction_MYYear_9Month_1
Interaction_MYYear_9Month_2
Interaction_MYYear_9Month_3
Interaction_MYYear_9Month_4
Interaction_MYYear_9Month_5
Interaction_MYYear_9Month_6
Interaction_MYYear_9Month_8
Interaction_MYYear_9Month_9
Interaction_MYYear_9Month_10
Interaction_MYYear_9Month_11
Interaction_MYYear_10Month_1
Interaction_MYYear_10Month_3
Interaction_MYYear_10Month_4
Interaction_MYYear_10Month_5
Interaction_MYYear_10Month_7
Interaction_MYYear_10Month_8
Interaction_MYYear_10Month_12
Interaction_MYYear_11Month_1
Interaction_MYYear_11Month_2
Interaction_MYYear_11Month_3
Interaction_MYYear_11Month_5
Interaction_MYYear_11Month_7
Interaction_MYYear_11Month_8
Interaction_MYYear_11Month_9
Interaction_MYYear_11Month_12
Interaction_MYYear_12Month_1
Interaction_MYYear_12Month_2
Interaction_MYYear_12Month_6
Interaction_MYYear_12Month_7
Interaction_MYYear_12Month_9
Interaction_MYYear_12Month_10
Interaction_MYYear_12Month_11
Interaction_MYYear_12Month_12
Interaction_MYYear_13Month_2
Interaction_MYYear_13Month_4
Interaction_MYYear_13Month_6
Interaction_MYYear_13Month_11
Interaction_MYYear_13Month_12
Interaction_MYYear_14Month_1
Interaction_MYYear_14Month_4
Interaction_MYYear_14Month_5
Interaction_MYYear_14Month_6
Interaction_MYYear_14Month_9
Interaction_MYYear_14Month_11
Interaction_MYYear_15Month_6
Interaction_MYYear_18Month_10
Interaction_MYYear_18Month_11
Interaction_MYYear_18Month_12
Wall time: 3min 2s

l=[]
for col in df.columns:
    if col in ['MachineIdentifier','HasDetections']:
        continue
    else:
        l.append(col)
l=['MachineIdentifier']+l+['HasDetections']

df=df[l]

#train=df.sample(frac=0.7,random_state=1)

#train.drop(columns=['AutoSampleOptIn','IsBeta','Census_IsPortableOperatingSystem','Census_IsFlightsDisabled'],inplace=True)
#df.drop(columns=['AutoSampleOptIn','IsBeta','Census_IsPortableOperatingSystem','Census_IsFlightsDisabled'],inplace=True)

df_test.drop(columns=['AutoSampleOptIn','IsBeta','Census_IsPortableOperatingSystem','Census_IsFlightsDisabled'],inplace=True)

df.shape

#df.drop(columns=categorical_columns,inplace=True)
#df_test.drop(columns=categorical_columns,inplace=True)

df.drop(columns=['AvSigVersion'],inplace=True)
#train.drop(columns=['AvSigVersion'],inplace=True)

df_test.drop(columns=['AvSigVersion'],inplace=True)

train=df.sample(frac=0.7,random_state=1)

X=train.iloc[:,1:-1]
y=train.iloc[:,-1]

from mlxtend.plotting import plot_learning_curves
from mlxtend.plotting import plot_decision_regions
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import BaggingClassifier
import matplotlib.gridspec as gridspec
from sklearn.model_selection import cross_val_score, train_test_split

from sklearn.preprocessing import StandardScaler

ss=StandardScaler()

ss.fit(X)

StandardScaler(copy=True, with_mean=True, with_std=True)

X=pd.DataFrame(data=ss.transform(X),columns=X.columns.tolist())

set(X.columns)-set(df_test.columns)

set()

df_test=pd.DataFrame(data=ss.transform(df_test[X.columns]),columns=X.columns.tolist())

#X.drop(columns=['Platform_w10', 'Platform_w2016', 'Platform_w8'],inplace=True)

X.shape

(6244507, 112)

%%time
fig,ax=plt.subplots(figsize=(12,12))
corr=X.corr()

# plot the heatmap
sns.heatmap(corr,xticklabels=corr.columns,yticklabels=corr.columns,ax=ax)

Wall time: 2min 57s

Reference:https://datascience.stackexchange.com/questions/10459/calculation-and-visualization-of-correlation-matrix-with-pandas

cmap=sns.diverging_palette(5, 250, as_cmap=True)
def magnify():
    return [dict(selector="th",
                 props=[("font-size", "7pt")]),
            dict(selector="td",
                 props=[('padding', "0em 0em")]),
            dict(selector="th:hover",
                 props=[("font-size", "12pt")]),
            dict(selector="tr:hover td:hover",
                 props=[('max-width', '200px'),
                        ('font-size', '12pt')])
]

corr.style.background_gradient(cmap, axis=1)\
    .set_properties(**{'max-width': '80px', 'font-size': '10pt'})\
    .set_caption("Hover to magify")\
    .set_precision(2)\
    .set_table_styles(magnify())

C:\Users\gandh\Anaconda3\lib\site-packages\matplotlib\colors.py:504: RuntimeWarning: invalid value encountered in less
  xa[xa < 0] = -1

#X.drop(columns=['HasTpm','AV_mediumrisk','AV_update_status''Interaction_P_OsVer_01','Interaction_P_OsVer_02','Interaction_P_OsVer_03'],inplace=True)
#df_test.drop(columns=['HasTpm','AV_mediumrisk','AV_update_status''Interaction_P_OsVer_01','Interaction_P_OsVer_02','Interaction_P_OsVer_03'],inplace=True)

X[['Census_OSBuildRevision','Census_OSSkuName']].head()

#df.drop(columns=['OsSuite','OsBuild','OsVer','Census_OSArchitecture','Census_OSBuildRevision','Census_OSSkuName'],inplace=True)
#df_test.drop(columns=['OsSuite','OsBuild','OsVer','Census_OSArchitecture','Census_OSBuildRevision','Census_OSSkuName'],inplace=True)

#X.drop(columns=['OsSuite','OsBuild','OsVer','Census_OSArchitecture','Census_OSBuildRevision','Census_OSSkuName'],inplace=True)

df_test.drop(columns=['Census_OSBuildRevision','Census_OSSkuName'],inplace=True)
X.drop(columns=['Census_OSBuildRevision','Census_OSSkuName'],inplace=True)

%%time
for col in X.columns:
    if X[col].nunique()==1:
        print(col)
        X.drop(columns=[col],inplace=True)
        df_test.drop(columns=[col],inplace=True)
    else:
        continue

Census_IsWIMBootEnabled
Wall time: 39.5 s

%%time
fig,ax=plt.subplots(figsize=(12,12))
corr=X.corr()

# plot the heatmap
sns.heatmap(corr,xticklabels=corr.columns,yticklabels=corr.columns,ax=ax)

Wall time: 3min 6s

%%time
cmap=sns.diverging_palette(5, 250, as_cmap=True)
def magnify():
    return [dict(selector="th",
                 props=[("font-size", "7pt")]),
            dict(selector="td",
                 props=[('padding', "0em 0em")]),
            dict(selector="th:hover",
                 props=[("font-size", "12pt")]),
            dict(selector="tr:hover td:hover",
                 props=[('max-width', '200px'),
                        ('font-size', '12pt')])
]

corr.style.background_gradient(cmap, axis=1)\
    .set_properties(**{'max-width': '80px', 'font-size': '10pt'})\
    .set_caption("Hover to magify")\
    .set_precision(2)\
    .set_table_styles(magnify())

Wall time: 40.9 ms

%%time
#X.drop(columns=['HasTpm','IeVerIdentifier','AV_update_status','Interaction_03','LocaleEnglishNameIdentifier','OsPlatformSubRelease','IsProtected','Census_OSVersion','Census_MDC2FormFactor','Device_PossibleOwnership','Census_OSBuildNumber','Platform','Census_OSInstallLanguageIdentifier'],inplace=True)
#df_test.drop(columns=['HasTpm','IeVerIdentifier','AV_update_status','Interaction_03','LocaleEnglishNameIdentifier','OsPlatformSubRelease','IsProtected','Census_OSVersion','Census_MDC2FormFactor','Device_PossibleOwnership','Census_OSBuildNumber','Platform','Census_OSInstallLanguageIdentifier'],inplace=True)

Wall time: 0 ns

#X.drop(columns=['IeVerIdentifier_encode','Census_MDC2FormFactor_encode'],inplace=True)
#df_test.drop(columns=['IeVerIdentifier_encode','Census_MDC2FormFactor_encode'],inplace=True)

X.shape

(6244507, 109)

from sklearn.model_selection import train_test_split

X_train,X_test,y_train,y_test=train_test_split(X.values,y.values,test_size=0.3,random_state=42)

X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1)

from sklearn.metrics import accuracy_score,precision_score,recall_score,confusion_matrix,roc_auc_score,roc_curve
from sklearn.tree import DecisionTreeClassifier

clf1 = DecisionTreeClassifier(criterion='entropy', max_depth=5)
clf2 = KNeighborsClassifier(n_neighbors=5)    

bagging1 = BaggingClassifier(base_estimator=clf1, n_estimators=10, max_samples=0.8, max_features=0.9)
bagging2 = BaggingClassifier(base_estimator=clf2, n_estimators=10, max_samples=0.8, max_features=0.9)

import itertools

%%time
bagging1.fit(X_train,y_train)

Wall time: 12min 47s

BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=5,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best'),
         bootstrap=True, bootstrap_features=False, max_features=0.9,
         max_samples=0.8, n_estimators=10, n_jobs=1, oob_score=False,
         random_state=None, verbose=0, warm_start=False)

predictions=bagging1.predict(X_test)

prediction_scores=bagging1.predict_proba(X_test)[:,1]

print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,prediction_scores))

Confusion Matrix for the Model : 
 [[459220 478785]
 [245312 690036]]
Accuracy of the model :  0.6134754101335946
Precision of the Model :  0.5903692695459783
Recall score of the Model :  0.7377318388450074
Area under ROC curve for the Model :  0.6683681213065038

%%time
#bagging2.fit(X_train,y_train)

#predictions=bagging2.predict(X_test)

#prediction_scores=bagging2.predict_proba(X_test)[:,1]

print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,prediction_scores))

from lightgbm import LGBMClassifier

clf=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15 ,max_depth=-1,
        min_child_samples=50, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=100, n_jobs=-1, num_leaves=500, objective=None,
        random_state=42, reg_alpha=0, reg_lambda=0.0, silent=False,
        subsample=0.9,subsample_freq=1)

%%time
clf.fit(X_train,y_train)

Wall time: 1min 55s

LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15, max_depth=-1,
        min_child_samples=50, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=100, n_jobs=-1, num_leaves=500, objective=None,
        random_state=42, reg_alpha=0, reg_lambda=0.0, silent=False,
        subsample=0.9, subsample_for_bin=200000, subsample_freq=1)

predictions=clf.predict(X_test)
prediction_scores=clf.predict_proba(X_test)
scores=[]
for score in prediction_scores:
    scores.append(score[1])

C:\Users\gandh\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,scores))

Confusion Matrix for the Model : 
 [[447455 221472]
 [231852 437330]]
Accuracy of the model :  0.6612204237472433
Precision of the Model :  0.6638261571762077
Recall score of the Model :  0.6535292341993658
Area under ROC curve for the Model :  0.7264697348603679

prediction_scores=clf.predict_proba(df_test[X.columns])
scores=[]
for score in prediction_scores:
    scores.append(score[1])

%%time
solution['HasDetections']=scores
solution.to_csv('submit_31st.csv',index=False)

Wall time: 1min 4s

solution.head()

temp=pd.DataFrame()

temp['features']=X_train.columns[np.argsort(clf.feature_importances_)]

temp['importance']=np.sort(clf.feature_importances_)

temp.sort_values(by=['importance'],ascending=False,inplace=True)

temp

important_columns=temp[temp.importance>0]['features'].tolist()

from sklearn.feature_selection import RFE

#selector = RFE(clf, 75, step=1,verbose=True)

%%time
#selector=selector.fit(X_train, y_train)

Wall time: 0 ns

#print(selector.support_)

#print(selector.ranking_)

clf2=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15, max_depth=-1,
        min_child_samples=2, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=500, n_jobs=-1, num_leaves=500, objective=None,
        random_state=42, reg_alpha=0.7, reg_lambda=0.7, silent=False,
        subsample=0.9, subsample_for_bin=100000, subsample_freq=1)

%%time
clf2.fit(X_train[important_columns],y_train)

Wall time: 4min 28s

LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15, max_depth=-1,
        min_child_samples=2, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=500, n_jobs=-1, num_leaves=500, objective=None,
        random_state=42, reg_alpha=0.7, reg_lambda=0.7, silent=False,
        subsample=0.9, subsample_for_bin=100000, subsample_freq=1)

predictions=clf2.predict(X_test[important_columns])
prediction_scores=clf2.predict_proba(X_test[important_columns])
scores=[]
for score in prediction_scores:
    scores.append(score[1])

C:\Users\gandh\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,scores))

Confusion Matrix for the Model : 
 [[444490 224437]
 [230343 438839]]
Accuracy of the model :  0.6601323210590467
Precision of the Model :  0.6616235172085225
Recall score of the Model :  0.6557842261148686
Area under ROC curve for the Model :  0.7247767926123824

prediction_scores=clf2.predict_proba(df_test[important_columns])
scores=[]
for score in prediction_scores:
    scores.append(score[1])

%%time
solution['HasDetections']=scores
solution.to_csv('submit_31st.csv',index=False)

Wall time: 30.8 s

clf3=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15, max_depth=-1,
        min_child_samples=2, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=1000, n_jobs=-1, num_leaves=100, objective=None,
        random_state=42, reg_alpha=0.9, reg_lambda=0.9, silent=False,
        subsample=0.9, subsample_for_bin=200000, subsample_freq=1)

%%time
clf2.fit(X_train[important_columns],y_train)

Wall time: 5min 15s

LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15, max_depth=-1,
        min_child_samples=2, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=500, n_jobs=-1, num_leaves=500, objective=None,
        random_state=42, reg_alpha=0.7, reg_lambda=0.7, silent=False,
        subsample=0.9, subsample_for_bin=100000, subsample_freq=1)

predictions=clf2.predict(X_test[important_columns])
prediction_scores=clf2.predict_proba(X_test[important_columns])
scores=[]
for score in prediction_scores:
    scores.append(score[1])

C:\Users\gandh\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,scores))

Confusion Matrix for the Model : 
 [[444490 224437]
 [230343 438839]]
Accuracy of the model :  0.6601323210590467
Precision of the Model :  0.6616235172085225
Recall score of the Model :  0.6557842261148686
Area under ROC curve for the Model :  0.7247767926123824

#clf=DecisionTreeClassifier(random_state=42)

import keras

Using TensorFlow backend.

from keras.models import Sequential
from keras.layers import Dense
from keras import layers
# Cross-validation
from sklearn.model_selection import KFold, StratifiedKFold, KFold #for K-fold cross validation
from sklearn.model_selection import cross_val_score #score evaluation
from sklearn.model_selection import cross_val_predict #prediction
from sklearn.model_selection import cross_validate
from keras.callbacks import EarlyStopping, ModelCheckpoint
# fix random seed for reproducibility
np.random.seed(7)

len(important_columns)

98

X.shape

(4460362, 109)

classifier = Sequential()
#First Hidden Layer
classifier.add(layers.Dropout(0.5, input_shape=(109,)),)
#Second  Hidden Layer
classifier.add(layers.Dense(units=20, activation='relu'))
# Add a dropout layer for previous hidden layer
classifier.add(layers.Dropout(0.5))
#classifier.add(layers.Dense(units=20, activation='relu'))
#classifier.add(layers.MaxPool1D(pool_size=4,data_format='channels_first'))
#classifier.add(layers.Dropout(0.5))
#classifier.add(layers.Dense(units=10, activation='relu'))
#classifier.add(layers.Dropout(0.5))
#Output Layer
classifier.add(Dense(1, activation='sigmoid', kernel_initializer='random_normal'))

#Compiling the neural network
classifier.compile(optimizer ='adam',loss='binary_crossentropy', metrics =['accuracy'])

# Set callback functions to early stop training and save the best model so far
callbacks1 = [EarlyStopping(monitor='val_loss', patience=5,min_delta=0,verbose=1),ModelCheckpoint(filepath='best_model2.h5', monitor='val_loss', save_best_only=True)]

# Set callback functions to early stop training and save the best model so far
callbacks2 = [EarlyStopping(monitor='val_loss', patience=10,min_delta=0,verbose=1),ModelCheckpoint(filepath='best_model3.h5', monitor='val_loss', save_best_only=True)]

%%time
# Fit the model
classifier.fit(X_train, y_train, epochs=100, batch_size=10000,validation_data=(X_val, y_val),callbacks=callbacks1)

Train on 2497802 samples, validate on 624451 samples
Epoch 1/100
2210000/2497802 [=========================>....] - ETA: 4:29 - loss: 0.7096 - acc: 0.493 - ETA: 2:31 - loss: 0.7085 - acc: 0.495 - ETA: 1:48 - loss: 0.7072 - acc: 0.495 - ETA: 1:24 - loss: 0.7065 - acc: 0.496 - ETA: 1:10 - loss: 0.7056 - acc: 0.498 - ETA: 1:01 - loss: 0.7054 - acc: 0.498 - ETA: 55s - loss: 0.7053 - acc: 0.498 - ETA: 50s - loss: 0.7053 - acc: 0.49 - ETA: 45s - loss: 0.7050 - acc: 0.49 - ETA: 43s - loss: 0.7048 - acc: 0.49 - ETA: 40s - loss: 0.7043 - acc: 0.50 - ETA: 38s - loss: 0.7036 - acc: 0.50 - ETA: 36s - loss: 0.7029 - acc: 0.50 - ETA: 34s - loss: 0.7025 - acc: 0.50 - ETA: 33s - loss: 0.7023 - acc: 0.50 - ETA: 32s - loss: 0.7022 - acc: 0.50 - ETA: 31s - loss: 0.7019 - acc: 0.50 - ETA: 30s - loss: 0.7016 - acc: 0.50 - ETA: 29s - loss: 0.7014 - acc: 0.50 - ETA: 28s - loss: 0.7012 - acc: 0.50 - ETA: 27s - loss: 0.7010 - acc: 0.50 - ETA: 27s - loss: 0.7008 - acc: 0.50 - ETA: 26s - loss: 0.7005 - acc: 0.50 - ETA: 25s - loss: 0.7001 - acc: 0.50 - ETA: 25s - loss: 0.6998 - acc: 0.50 - ETA: 24s - loss: 0.6995 - acc: 0.51 - ETA: 24s - loss: 0.6992 - acc: 0.51 - ETA: 24s - loss: 0.6989 - acc: 0.51 - ETA: 23s - loss: 0.6988 - acc: 0.51 - ETA: 23s - loss: 0.6986 - acc: 0.51 - ETA: 22s - loss: 0.6984 - acc: 0.51 - ETA: 22s - loss: 0.6982 - acc: 0.51 - ETA: 22s - loss: 0.6980 - acc: 0.51 - ETA: 21s - loss: 0.6978 - acc: 0.51 - ETA: 21s - loss: 0.6976 - acc: 0.51 - ETA: 21s - loss: 0.6974 - acc: 0.51 - ETA: 21s - loss: 0.6971 - acc: 0.51 - ETA: 20s - loss: 0.6970 - acc: 0.51 - ETA: 20s - loss: 0.6968 - acc: 0.51 - ETA: 20s - loss: 0.6967 - acc: 0.51 - ETA: 20s - loss: 0.6965 - acc: 0.51 - ETA: 19s - loss: 0.6964 - acc: 0.51 - ETA: 19s - loss: 0.6962 - acc: 0.51 - ETA: 19s - loss: 0.6960 - acc: 0.51 - ETA: 19s - loss: 0.6958 - acc: 0.51 - ETA: 18s - loss: 0.6957 - acc: 0.51 - ETA: 18s - loss: 0.6956 - acc: 0.51 - ETA: 18s - loss: 0.6955 - acc: 0.52 - ETA: 18s - loss: 0.6953 - acc: 0.52 - ETA: 18s - loss: 0.6952 - acc: 0.52 - ETA: 17s - loss: 0.6950 - acc: 0.52 - ETA: 17s - loss: 0.6948 - acc: 0.52 - ETA: 17s - loss: 0.6947 - acc: 0.52 - ETA: 17s - loss: 0.6946 - acc: 0.52 - ETA: 17s - loss: 0.6945 - acc: 0.52 - ETA: 17s - loss: 0.6943 - acc: 0.52 - ETA: 16s - loss: 0.6942 - acc: 0.52 - ETA: 16s - loss: 0.6941 - acc: 0.52 - ETA: 16s - loss: 0.6940 - acc: 0.52 - ETA: 16s - loss: 0.6939 - acc: 0.52 - ETA: 16s - loss: 0.6938 - acc: 0.52 - ETA: 16s - loss: 0.6937 - acc: 0.52 - ETA: 16s - loss: 0.6935 - acc: 0.52 - ETA: 15s - loss: 0.6934 - acc: 0.52 - ETA: 15s - loss: 0.6933 - acc: 0.52 - ETA: 15s - loss: 0.6932 - acc: 0.52 - ETA: 15s - loss: 0.6931 - acc: 0.52 - ETA: 15s - loss: 0.6930 - acc: 0.52 - ETA: 14s - loss: 0.6928 - acc: 0.52 - ETA: 14s - loss: 0.6927 - acc: 0.52 - ETA: 14s - loss: 0.6926 - acc: 0.52 - ETA: 14s - loss: 0.6925 - acc: 0.52 - ETA: 14s - loss: 0.6924 - acc: 0.52 - ETA: 14s - loss: 0.6923 - acc: 0.52 - ETA: 14s - loss: 0.6922 - acc: 0.53 - ETA: 14s - loss: 0.6922 - acc: 0.53 - ETA: 13s - loss: 0.6921 - acc: 0.53 - ETA: 13s - loss: 0.6920 - acc: 0.53 - ETA: 13s - loss: 0.6919 - acc: 0.53 - ETA: 13s - loss: 0.6918 - acc: 0.53 - ETA: 13s - loss: 0.6917 - acc: 0.53 - ETA: 13s - loss: 0.6916 - acc: 0.53 - ETA: 13s - loss: 0.6915 - acc: 0.53 - ETA: 13s - loss: 0.6915 - acc: 0.53 - ETA: 12s - loss: 0.6914 - acc: 0.53 - ETA: 12s - loss: 0.6913 - acc: 0.53 - ETA: 12s - loss: 0.6912 - acc: 0.53 - ETA: 12s - loss: 0.6911 - acc: 0.53 - ETA: 12s - loss: 0.6910 - acc: 0.53 - ETA: 12s - loss: 0.6909 - acc: 0.53 - ETA: 12s - loss: 0.6908 - acc: 0.53 - ETA: 12s - loss: 0.6907 - acc: 0.53 - ETA: 12s - loss: 0.6907 - acc: 0.53 - ETA: 12s - loss: 0.6906 - acc: 0.53 - ETA: 11s - loss: 0.6905 - acc: 0.53 - ETA: 11s - loss: 0.6905 - acc: 0.53 - ETA: 11s - loss: 0.6904 - acc: 0.53 - ETA: 11s - loss: 0.6903 - acc: 0.53 - ETA: 11s - loss: 0.6902 - acc: 0.53 - ETA: 11s - loss: 0.6901 - acc: 0.53 - ETA: 11s - loss: 0.6901 - acc: 0.53 - ETA: 11s - loss: 0.6900 - acc: 0.53 - ETA: 11s - loss: 0.6899 - acc: 0.53 - ETA: 11s - loss: 0.6898 - acc: 0.53 - 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ETA: 2s - loss: 0.6837 - acc: 0.554 - ETA: 2s - loss: 0.6836 - acc: 0.554 - ETA: 1s - loss: 0.6836 - acc: 0.5542497802/2497802 [==============================] - ETA: 1s - loss: 0.6835 - acc: 0.554 - ETA: 1s - loss: 0.6835 - acc: 0.554 - ETA: 1s - loss: 0.6835 - acc: 0.554 - ETA: 1s - loss: 0.6834 - acc: 0.554 - ETA: 1s - loss: 0.6833 - acc: 0.555 - ETA: 1s - loss: 0.6833 - acc: 0.555 - ETA: 1s - loss: 0.6833 - acc: 0.555 - ETA: 1s - loss: 0.6832 - acc: 0.555 - ETA: 1s - loss: 0.6832 - acc: 0.555 - ETA: 1s - loss: 0.6832 - acc: 0.555 - ETA: 1s - loss: 0.6831 - acc: 0.555 - ETA: 1s - loss: 0.6831 - acc: 0.555 - ETA: 0s - loss: 0.6830 - acc: 0.555 - ETA: 0s - loss: 0.6830 - acc: 0.556 - ETA: 0s - loss: 0.6829 - acc: 0.556 - ETA: 0s - loss: 0.6829 - acc: 0.556 - ETA: 0s - loss: 0.6828 - acc: 0.556 - ETA: 0s - loss: 0.6828 - acc: 0.556 - ETA: 0s - loss: 0.6828 - acc: 0.556 - ETA: 0s - loss: 0.6827 - acc: 0.556 - ETA: 0s - loss: 0.6827 - acc: 0.556 - ETA: 0s - loss: 0.6827 - acc: 0.556 - ETA: 0s - loss: 0.6826 - acc: 0.556 - ETA: 0s - loss: 0.6826 - acc: 0.556 - ETA: 0s - loss: 0.6826 - acc: 0.557 - ETA: 0s - loss: 0.6825 - acc: 0.557 - ETA: 0s - loss: 0.6825 - acc: 0.557 - 18s 7us/step - loss: 0.6824 - acc: 0.5572 - val_loss: 0.6614 - val_acc: 0.6152
Epoch 2/100
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acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.58262497802/2497802 [==============================] - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6711 - acc: 0.582 - ETA: 1s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.583 - 16s 6us/step - loss: 0.6710 - acc: 0.5830 - val_loss: 0.6562 - val_acc: 0.6194
Epoch 3/100
2150000/2497802 [========================>.....] - ETA: 32s - loss: 0.6699 - acc: 0.58 - ETA: 23s - loss: 0.6708 - acc: 0.58 - ETA: 20s - loss: 0.6719 - acc: 0.58 - ETA: 18s - loss: 0.6712 - acc: 0.58 - ETA: 17s - loss: 0.6708 - acc: 0.58 - ETA: 17s - loss: 0.6702 - acc: 0.58 - ETA: 16s - loss: 0.6706 - acc: 0.58 - ETA: 16s - loss: 0.6704 - acc: 0.58 - ETA: 15s - loss: 0.6707 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6703 - acc: 0.58 - ETA: 14s - loss: 0.6704 - acc: 0.58 - ETA: 14s - loss: 0.6705 - acc: 0.58 - ETA: 14s - loss: 0.6705 - acc: 0.58 - ETA: 14s - loss: 0.6704 - acc: 0.58 - ETA: 14s - loss: 0.6704 - acc: 0.58 - ETA: 14s - loss: 0.6701 - acc: 0.58 - ETA: 14s - loss: 0.6701 - acc: 0.58 - ETA: 14s - loss: 0.6700 - acc: 0.58 - ETA: 14s - loss: 0.6700 - acc: 0.58 - 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acc: 0.586 - ETA: 2s - loss: 0.6693 - acc: 0.586 - ETA: 2s - loss: 0.6693 - acc: 0.586 - ETA: 2s - loss: 0.6693 - acc: 0.586 - ETA: 2s - loss: 0.6693 - acc: 0.58642497802/2497802 [==============================] - ETA: 2s - loss: 0.6693 - acc: 0.586 - ETA: 2s - loss: 0.6693 - acc: 0.586 - ETA: 2s - loss: 0.6693 - acc: 0.586 - ETA: 2s - loss: 0.6693 - acc: 0.586 - ETA: 1s - loss: 0.6693 - acc: 0.586 - ETA: 1s - loss: 0.6693 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 1s - loss: 0.6693 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 1s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6692 - acc: 0.586 - ETA: 0s - loss: 0.6691 - acc: 0.586 - 17s 7us/step - loss: 0.6692 - acc: 0.5867 - val_loss: 0.6550 - val_acc: 0.6210
Epoch 4/100
2150000/2497802 [========================>.....] - ETA: 37s - loss: 0.6693 - acc: 0.58 - ETA: 25s - loss: 0.6699 - acc: 0.58 - ETA: 21s - loss: 0.6690 - acc: 0.58 - ETA: 20s - loss: 0.6688 - acc: 0.58 - ETA: 19s - loss: 0.6690 - acc: 0.58 - ETA: 19s - loss: 0.6688 - acc: 0.59 - ETA: 19s - loss: 0.6687 - acc: 0.59 - ETA: 18s - loss: 0.6687 - acc: 0.58 - ETA: 18s - loss: 0.6687 - acc: 0.58 - ETA: 18s - loss: 0.6689 - acc: 0.58 - ETA: 17s - loss: 0.6686 - acc: 0.58 - ETA: 17s - loss: 0.6686 - acc: 0.58 - ETA: 17s - loss: 0.6685 - acc: 0.58 - ETA: 17s - loss: 0.6685 - acc: 0.58 - ETA: 17s - loss: 0.6687 - acc: 0.58 - ETA: 17s - loss: 0.6686 - acc: 0.58 - ETA: 16s - loss: 0.6685 - acc: 0.58 - ETA: 16s - loss: 0.6684 - acc: 0.58 - ETA: 16s - loss: 0.6687 - acc: 0.58 - ETA: 16s - loss: 0.6687 - acc: 0.58 - ETA: 16s - loss: 0.6688 - acc: 0.58 - ETA: 16s - loss: 0.6689 - acc: 0.58 - ETA: 16s - loss: 0.6687 - acc: 0.58 - ETA: 16s - loss: 0.6688 - acc: 0.58 - ETA: 15s - loss: 0.6687 - acc: 0.58 - 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ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - 18s 7us/step - loss: 0.6684 - acc: 0.5885 - val_loss: 0.6545 - val_acc: 0.6218
Epoch 5/100
2150000/2497802 [========================>.....] - ETA: 37s - loss: 0.6758 - acc: 0.57 - ETA: 25s - loss: 0.6707 - acc: 0.58 - ETA: 23s - loss: 0.6695 - acc: 0.58 - ETA: 21s - loss: 0.6693 - acc: 0.58 - ETA: 20s - loss: 0.6692 - acc: 0.58 - ETA: 19s - loss: 0.6691 - acc: 0.58 - ETA: 19s - loss: 0.6692 - acc: 0.58 - ETA: 18s - loss: 0.6689 - acc: 0.58 - ETA: 18s - loss: 0.6691 - acc: 0.58 - ETA: 18s - loss: 0.6692 - acc: 0.58 - ETA: 17s - loss: 0.6694 - acc: 0.58 - ETA: 17s - loss: 0.6689 - acc: 0.58 - ETA: 17s - loss: 0.6688 - acc: 0.58 - ETA: 17s - loss: 0.6687 - acc: 0.58 - ETA: 17s - loss: 0.6687 - acc: 0.58 - ETA: 16s - loss: 0.6687 - acc: 0.58 - ETA: 16s - loss: 0.6689 - acc: 0.58 - ETA: 16s - loss: 0.6689 - acc: 0.58 - ETA: 16s - loss: 0.6690 - acc: 0.58 - ETA: 16s - loss: 0.6691 - acc: 0.58 - ETA: 16s - loss: 0.6690 - acc: 0.58 - ETA: 16s - loss: 0.6690 - acc: 0.58 - ETA: 16s - loss: 0.6689 - acc: 0.58 - ETA: 16s - loss: 0.6691 - acc: 0.58 - ETA: 16s - loss: 0.6690 - acc: 0.58 - 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Epoch 6/100
2150000/2497802 [========================>.....] - ETA: 36s - loss: 0.6670 - acc: 0.59 - ETA: 25s - loss: 0.6664 - acc: 0.59 - ETA: 21s - loss: 0.6675 - acc: 0.59 - ETA: 20s - loss: 0.6672 - acc: 0.59 - ETA: 19s - loss: 0.6667 - acc: 0.59 - ETA: 19s - loss: 0.6669 - acc: 0.59 - ETA: 19s - loss: 0.6671 - acc: 0.59 - ETA: 18s - loss: 0.6674 - acc: 0.59 - ETA: 18s - loss: 0.6674 - acc: 0.59 - ETA: 18s - loss: 0.6676 - acc: 0.58 - ETA: 18s - loss: 0.6674 - acc: 0.59 - ETA: 18s - loss: 0.6672 - acc: 0.59 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6672 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 16s - loss: 0.6673 - acc: 0.58 - ETA: 16s - loss: 0.6674 - acc: 0.58 - ETA: 16s - loss: 0.6675 - acc: 0.58 - ETA: 16s - loss: 0.6675 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - 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acc: 0.589 - ETA: 2s - loss: 0.6675 - acc: 0.589 - ETA: 2s - loss: 0.6675 - acc: 0.589 - ETA: 2s - loss: 0.6675 - acc: 0.589 - ETA: 2s - loss: 0.6675 - acc: 0.58972497802/2497802 [==============================] - ETA: 2s - loss: 0.6675 - acc: 0.589 - ETA: 2s - loss: 0.6675 - acc: 0.589 - ETA: 2s - loss: 0.6674 - acc: 0.589 - ETA: 2s - loss: 0.6675 - acc: 0.589 - ETA: 2s - loss: 0.6675 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6675 - acc: 0.589 - ETA: 1s - loss: 0.6675 - acc: 0.589 - ETA: 1s - loss: 0.6675 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6675 - acc: 0.589 - ETA: 1s - loss: 0.6675 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6675 - acc: 0.589 - ETA: 1s - loss: 0.6675 - acc: 0.589 - ETA: 0s - loss: 0.6675 - acc: 0.589 - ETA: 0s - loss: 0.6675 - acc: 0.589 - ETA: 0s - loss: 0.6675 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6675 - acc: 0.589 - ETA: 0s - loss: 0.6675 - acc: 0.589 - ETA: 0s - loss: 0.6675 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6675 - acc: 0.589 - ETA: 0s - loss: 0.6675 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6675 - acc: 0.589 - 18s 7us/step - loss: 0.6675 - acc: 0.5897 - val_loss: 0.6539 - val_acc: 0.6224
Epoch 7/100
2150000/2497802 [========================>.....] - ETA: 37s - loss: 0.6685 - acc: 0.58 - ETA: 26s - loss: 0.6674 - acc: 0.59 - ETA: 22s - loss: 0.6675 - acc: 0.59 - ETA: 20s - loss: 0.6686 - acc: 0.58 - ETA: 19s - loss: 0.6684 - acc: 0.58 - ETA: 19s - loss: 0.6684 - acc: 0.58 - ETA: 18s - loss: 0.6683 - acc: 0.58 - ETA: 18s - loss: 0.6684 - acc: 0.58 - ETA: 18s - loss: 0.6686 - acc: 0.58 - ETA: 18s - loss: 0.6684 - acc: 0.58 - ETA: 17s - loss: 0.6684 - acc: 0.58 - ETA: 17s - loss: 0.6684 - acc: 0.58 - ETA: 17s - loss: 0.6684 - acc: 0.58 - ETA: 17s - loss: 0.6684 - acc: 0.58 - ETA: 17s - loss: 0.6682 - acc: 0.58 - ETA: 17s - loss: 0.6682 - acc: 0.58 - ETA: 16s - loss: 0.6680 - acc: 0.58 - ETA: 16s - loss: 0.6679 - acc: 0.58 - ETA: 16s - loss: 0.6678 - acc: 0.59 - ETA: 16s - loss: 0.6677 - acc: 0.59 - ETA: 16s - loss: 0.6676 - acc: 0.59 - ETA: 16s - loss: 0.6676 - acc: 0.59 - ETA: 16s - loss: 0.6675 - acc: 0.59 - ETA: 15s - loss: 0.6675 - acc: 0.59 - ETA: 15s - loss: 0.6676 - acc: 0.58 - 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Epoch 8/100
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Epoch 9/100
2150000/2497802 [========================>.....] - ETA: 33s - loss: 0.6650 - acc: 0.59 - ETA: 24s - loss: 0.6652 - acc: 0.59 - ETA: 21s - loss: 0.6657 - acc: 0.59 - ETA: 19s - loss: 0.6664 - acc: 0.59 - ETA: 19s - loss: 0.6667 - acc: 0.59 - ETA: 18s - loss: 0.6670 - acc: 0.59 - ETA: 18s - loss: 0.6671 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.59 - ETA: 17s - loss: 0.6675 - acc: 0.58 - ETA: 17s - loss: 0.6676 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.59 - ETA: 17s - loss: 0.6671 - acc: 0.59 - ETA: 17s - loss: 0.6674 - acc: 0.58 - ETA: 17s - loss: 0.6675 - acc: 0.58 - ETA: 17s - loss: 0.6676 - acc: 0.58 - ETA: 17s - loss: 0.6677 - acc: 0.58 - ETA: 17s - loss: 0.6676 - acc: 0.58 - ETA: 17s - loss: 0.6678 - acc: 0.58 - ETA: 17s - loss: 0.6678 - acc: 0.58 - ETA: 16s - loss: 0.6677 - acc: 0.58 - ETA: 16s - loss: 0.6677 - acc: 0.58 - ETA: 16s - loss: 0.6677 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - 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acc: 0.589 - ETA: 2s - loss: 0.6671 - acc: 0.589 - ETA: 2s - loss: 0.6671 - acc: 0.589 - ETA: 2s - loss: 0.6671 - acc: 0.589 - ETA: 2s - loss: 0.6671 - acc: 0.58982497802/2497802 [==============================] - ETA: 2s - loss: 0.6671 - acc: 0.589 - ETA: 2s - loss: 0.6671 - acc: 0.589 - ETA: 2s - loss: 0.6671 - acc: 0.589 - ETA: 2s - loss: 0.6671 - acc: 0.589 - ETA: 2s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - 18s 7us/step - loss: 0.6671 - acc: 0.5897 - val_loss: 0.6538 - val_acc: 0.6232
Epoch 10/100
2150000/2497802 [========================>.....] - ETA: 37s - loss: 0.6687 - acc: 0.58 - ETA: 26s - loss: 0.6671 - acc: 0.58 - ETA: 22s - loss: 0.6678 - acc: 0.58 - ETA: 21s - loss: 0.6668 - acc: 0.58 - ETA: 20s - loss: 0.6670 - acc: 0.58 - ETA: 19s - loss: 0.6673 - acc: 0.58 - ETA: 19s - loss: 0.6670 - acc: 0.58 - ETA: 18s - loss: 0.6668 - acc: 0.58 - ETA: 18s - loss: 0.6670 - acc: 0.58 - ETA: 18s - loss: 0.6672 - acc: 0.58 - ETA: 17s - loss: 0.6674 - acc: 0.58 - ETA: 17s - loss: 0.6676 - acc: 0.58 - ETA: 17s - loss: 0.6675 - acc: 0.58 - ETA: 17s - loss: 0.6674 - acc: 0.58 - ETA: 16s - loss: 0.6674 - acc: 0.58 - ETA: 16s - loss: 0.6671 - acc: 0.58 - ETA: 16s - loss: 0.6671 - acc: 0.58 - ETA: 16s - loss: 0.6671 - acc: 0.58 - ETA: 16s - loss: 0.6670 - acc: 0.59 - ETA: 16s - loss: 0.6668 - acc: 0.59 - ETA: 16s - loss: 0.6668 - acc: 0.59 - ETA: 16s - loss: 0.6667 - acc: 0.59 - ETA: 16s - loss: 0.6667 - acc: 0.59 - ETA: 16s - loss: 0.6668 - acc: 0.59 - ETA: 15s - loss: 0.6668 - acc: 0.59 - 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acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6668 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 11s - loss: 0.6668 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - 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acc: 0.5900 - ETA: 9s - loss: 0.6669 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6669 - acc: 0.590 - ETA: 9s - loss: 0.6669 - acc: 0.590 - ETA: 8s - loss: 0.6669 - acc: 0.590 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6670 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - 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acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 6s - loss: 0.6668 - acc: 0.590 - ETA: 6s - loss: 0.6668 - acc: 0.589 - ETA: 6s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.58992497802/2497802 [==============================] - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - 18s 7us/step - loss: 0.6670 - acc: 0.5896 - val_loss: 0.6550 - val_acc: 0.6232
Epoch 11/100
2150000/2497802 [========================>.....] - ETA: 36s - loss: 0.6695 - acc: 0.58 - ETA: 26s - loss: 0.6671 - acc: 0.58 - ETA: 22s - loss: 0.6668 - acc: 0.58 - ETA: 21s - loss: 0.6673 - acc: 0.58 - ETA: 19s - loss: 0.6675 - acc: 0.58 - ETA: 19s - loss: 0.6673 - acc: 0.58 - ETA: 18s - loss: 0.6676 - acc: 0.58 - ETA: 18s - loss: 0.6677 - acc: 0.58 - ETA: 18s - loss: 0.6675 - acc: 0.59 - ETA: 17s - loss: 0.6674 - acc: 0.58 - ETA: 17s - loss: 0.6671 - acc: 0.58 - ETA: 17s - loss: 0.6670 - acc: 0.58 - ETA: 17s - loss: 0.6671 - acc: 0.58 - ETA: 16s - loss: 0.6672 - acc: 0.58 - ETA: 16s - loss: 0.6672 - acc: 0.58 - ETA: 16s - loss: 0.6671 - acc: 0.59 - ETA: 16s - loss: 0.6671 - acc: 0.59 - ETA: 16s - loss: 0.6671 - acc: 0.59 - ETA: 16s - loss: 0.6671 - acc: 0.59 - ETA: 16s - loss: 0.6672 - acc: 0.59 - ETA: 16s - loss: 0.6671 - acc: 0.59 - ETA: 15s - loss: 0.6670 - acc: 0.59 - ETA: 15s - loss: 0.6669 - acc: 0.58 - ETA: 15s - loss: 0.6670 - acc: 0.59 - ETA: 15s - loss: 0.6670 - acc: 0.59 - 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ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.590 - 18s 7us/step - loss: 0.6669 - acc: 0.5900 - val_loss: 0.6546 - val_acc: 0.6231
Epoch 12/100
2150000/2497802 [========================>.....] - ETA: 40s - loss: 0.6640 - acc: 0.59 - ETA: 28s - loss: 0.6669 - acc: 0.59 - ETA: 23s - loss: 0.6669 - acc: 0.58 - ETA: 21s - loss: 0.6668 - acc: 0.58 - ETA: 20s - loss: 0.6663 - acc: 0.59 - ETA: 19s - loss: 0.6670 - acc: 0.58 - ETA: 19s - loss: 0.6668 - acc: 0.58 - ETA: 18s - loss: 0.6666 - acc: 0.58 - ETA: 18s - loss: 0.6666 - acc: 0.58 - ETA: 18s - loss: 0.6667 - acc: 0.58 - ETA: 18s - loss: 0.6667 - acc: 0.58 - ETA: 18s - loss: 0.6669 - acc: 0.58 - ETA: 17s - loss: 0.6670 - acc: 0.58 - ETA: 17s - loss: 0.6670 - acc: 0.58 - ETA: 17s - loss: 0.6671 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6672 - acc: 0.58 - ETA: 16s - loss: 0.6673 - acc: 0.58 - ETA: 16s - loss: 0.6672 - acc: 0.58 - ETA: 16s - loss: 0.6670 - acc: 0.58 - ETA: 16s - loss: 0.6669 - acc: 0.58 - ETA: 16s - loss: 0.6669 - acc: 0.58 - ETA: 16s - loss: 0.6672 - acc: 0.58 - ETA: 16s - loss: 0.6675 - acc: 0.58 - ETA: 16s - loss: 0.6674 - acc: 0.58 - 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acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.58952497802/2497802 [==============================] - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6670 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - 18s 7us/step - loss: 0.6669 - acc: 0.5897 - val_loss: 0.6538 - val_acc: 0.6234
Epoch 00012: early stopping
Wall time: 3min 33s

<keras.callbacks.History at 0x26879bcb3c8>

%%time
# Fit the model
#classifier.fit(X_train, y_train, epochs=200, batch_size=500,validation_data=(X_val, y_val),callbacks=callbacks1)

Wall time: 0 ns

%%time
# Fit the model
#classifier.fit(X_train, y_train, epochs=1000, batch_size=5000,validation_data=(X_val, y_val),callbacks=callbacks1)

Wall time: 0 ns

%%time
# Fit the model
classifier.fit(X_train, y_train, epochs=1000, batch_size=50000,validation_data=(X_val, y_val),callbacks=callbacks2)

Train on 2497802 samples, validate on 624451 samples
Epoch 1/1000
2497802/2497802 [==============================] - ETA: 17s - loss: 0.6679 - acc: 0.58 - ETA: 15s - loss: 0.6672 - acc: 0.58 - ETA: 14s - loss: 0.6670 - acc: 0.59 - ETA: 14s - loss: 0.6672 - acc: 0.59 - ETA: 13s - loss: 0.6672 - acc: 0.59 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 12s - loss: 0.6674 - acc: 0.58 - ETA: 12s - loss: 0.6673 - acc: 0.58 - ETA: 12s - loss: 0.6672 - acc: 0.58 - ETA: 11s - loss: 0.6673 - acc: 0.58 - ETA: 11s - loss: 0.6673 - acc: 0.58 - ETA: 11s - loss: 0.6673 - acc: 0.58 - ETA: 11s - loss: 0.6673 - acc: 0.58 - ETA: 10s - loss: 0.6672 - acc: 0.58 - ETA: 10s - loss: 0.6672 - acc: 0.58 - ETA: 10s - loss: 0.6672 - acc: 0.58 - ETA: 9s - loss: 0.6671 - acc: 0.5899 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6670 - acc: 0.589 - ETA: 8s - loss: 0.6670 - acc: 0.589 - ETA: 7s - loss: 0.6669 - acc: 0.589 - ETA: 7s - loss: 0.6670 - acc: 0.589 - ETA: 7s - loss: 0.6669 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6669 - acc: 0.589 - ETA: 4s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6668 - acc: 0.589 - 16s 6us/step - loss: 0.6668 - acc: 0.5900 - val_loss: 0.6539 - val_acc: 0.6235
Epoch 2/1000
2497802/2497802 [==============================] - ETA: 19s - loss: 0.6661 - acc: 0.59 - ETA: 16s - loss: 0.6662 - acc: 0.59 - ETA: 15s - loss: 0.6661 - acc: 0.59 - ETA: 14s - loss: 0.6657 - acc: 0.59 - ETA: 14s - loss: 0.6659 - acc: 0.59 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6660 - acc: 0.59 - ETA: 11s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5904 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 8s - loss: 0.6669 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 8s - loss: 0.6669 - acc: 0.590 - ETA: 7s - loss: 0.6669 - acc: 0.590 - ETA: 7s - loss: 0.6669 - acc: 0.590 - ETA: 7s - loss: 0.6669 - acc: 0.590 - ETA: 6s - loss: 0.6669 - acc: 0.590 - ETA: 6s - loss: 0.6669 - acc: 0.590 - ETA: 6s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.590 - ETA: 4s - loss: 0.6669 - acc: 0.590 - ETA: 4s - loss: 0.6669 - acc: 0.590 - ETA: 4s - loss: 0.6669 - acc: 0.590 - ETA: 3s - loss: 0.6669 - acc: 0.590 - ETA: 3s - loss: 0.6669 - acc: 0.590 - ETA: 3s - loss: 0.6670 - acc: 0.590 - ETA: 2s - loss: 0.6670 - acc: 0.590 - ETA: 2s - loss: 0.6669 - acc: 0.590 - ETA: 2s - loss: 0.6669 - acc: 0.590 - ETA: 2s - loss: 0.6669 - acc: 0.590 - ETA: 1s - loss: 0.6669 - acc: 0.590 - ETA: 1s - loss: 0.6669 - acc: 0.590 - ETA: 1s - loss: 0.6670 - acc: 0.590 - ETA: 0s - loss: 0.6670 - acc: 0.590 - ETA: 0s - loss: 0.6670 - acc: 0.590 - ETA: 0s - loss: 0.6670 - acc: 0.590 - 16s 6us/step - loss: 0.6669 - acc: 0.5900 - val_loss: 0.6542 - val_acc: 0.6232
Epoch 3/1000
2497802/2497802 [==============================] - ETA: 19s - loss: 0.6676 - acc: 0.58 - ETA: 15s - loss: 0.6674 - acc: 0.58 - ETA: 14s - loss: 0.6676 - acc: 0.58 - ETA: 14s - loss: 0.6674 - acc: 0.58 - ETA: 13s - loss: 0.6674 - acc: 0.58 - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 13s - loss: 0.6670 - acc: 0.58 - ETA: 12s - loss: 0.6670 - acc: 0.58 - ETA: 12s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6672 - acc: 0.58 - ETA: 11s - loss: 0.6672 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 9s - loss: 0.6670 - acc: 0.5897 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 15s 6us/step - loss: 0.6667 - acc: 0.5900 - val_loss: 0.6541 - val_acc: 0.6235
Epoch 4/1000
2497802/2497802 [==============================] - ETA: 18s - loss: 0.6671 - acc: 0.58 - ETA: 15s - loss: 0.6668 - acc: 0.58 - ETA: 15s - loss: 0.6667 - acc: 0.58 - ETA: 14s - loss: 0.6668 - acc: 0.58 - ETA: 14s - loss: 0.6667 - acc: 0.58 - ETA: 13s - loss: 0.6670 - acc: 0.58 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5903 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 16s 6us/step - loss: 0.6667 - acc: 0.5903 - val_loss: 0.6539 - val_acc: 0.6233
Epoch 5/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6664 - acc: 0.59 - ETA: 16s - loss: 0.6670 - acc: 0.58 - ETA: 15s - loss: 0.6671 - acc: 0.58 - ETA: 14s - loss: 0.6670 - acc: 0.58 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 13s - loss: 0.6666 - acc: 0.59 - ETA: 13s - loss: 0.6666 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6665 - acc: 0.5908 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 16s 6us/step - loss: 0.6665 - acc: 0.5906 - val_loss: 0.6539 - val_acc: 0.6236
Epoch 6/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6657 - acc: 0.58 - ETA: 14s - loss: 0.6666 - acc: 0.58 - ETA: 14s - loss: 0.6663 - acc: 0.59 - ETA: 15s - loss: 0.6665 - acc: 0.59 - ETA: 14s - loss: 0.6666 - acc: 0.59 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 12s - loss: 0.6667 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6669 - acc: 0.59 - ETA: 9s - loss: 0.6669 - acc: 0.5901 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 6s - loss: 0.6668 - acc: 0.590 - ETA: 6s - loss: 0.6669 - acc: 0.590 - ETA: 6s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 4s - loss: 0.6669 - acc: 0.589 - ETA: 4s - loss: 0.6669 - acc: 0.589 - ETA: 4s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.590 - ETA: 1s - loss: 0.6669 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - 15s 6us/step - loss: 0.6669 - acc: 0.5899 - val_loss: 0.6541 - val_acc: 0.6236
Epoch 7/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6660 - acc: 0.58 - ETA: 14s - loss: 0.6661 - acc: 0.58 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6654 - acc: 0.59 - ETA: 13s - loss: 0.6659 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5908 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6668 - acc: 0.590 - ETA: 6s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 15s 6us/step - loss: 0.6667 - acc: 0.5902 - val_loss: 0.6537 - val_acc: 0.6237
Epoch 8/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6645 - acc: 0.59 - ETA: 14s - loss: 0.6653 - acc: 0.59 - ETA: 14s - loss: 0.6661 - acc: 0.58 - ETA: 15s - loss: 0.6662 - acc: 0.58 - ETA: 14s - loss: 0.6662 - acc: 0.59 - ETA: 14s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 9s - loss: 0.6664 - acc: 0.5906 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 16s 6us/step - loss: 0.6666 - acc: 0.5903 - val_loss: 0.6543 - val_acc: 0.6236
Epoch 9/1000
2497802/2497802 [==============================] - ETA: 21s - loss: 0.6661 - acc: 0.58 - ETA: 18s - loss: 0.6671 - acc: 0.58 - ETA: 16s - loss: 0.6666 - acc: 0.59 - ETA: 15s - loss: 0.6668 - acc: 0.59 - ETA: 15s - loss: 0.6668 - acc: 0.58 - ETA: 14s - loss: 0.6669 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 12s - loss: 0.6673 - acc: 0.58 - ETA: 12s - loss: 0.6673 - acc: 0.58 - ETA: 11s - loss: 0.6673 - acc: 0.58 - ETA: 11s - loss: 0.6672 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 10s - loss: 0.6669 - acc: 0.58 - ETA: 10s - loss: 0.6669 - acc: 0.58 - ETA: 9s - loss: 0.6667 - acc: 0.5895 - ETA: 9s - loss: 0.6667 - acc: 0.589 - ETA: 9s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6666 - acc: 0.589 - ETA: 5s - loss: 0.6666 - acc: 0.589 - ETA: 5s - loss: 0.6666 - acc: 0.589 - ETA: 4s - loss: 0.6666 - acc: 0.589 - ETA: 4s - loss: 0.6666 - acc: 0.589 - ETA: 4s - loss: 0.6666 - acc: 0.589 - ETA: 3s - loss: 0.6666 - acc: 0.589 - ETA: 3s - loss: 0.6665 - acc: 0.589 - ETA: 3s - loss: 0.6665 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - 15s 6us/step - loss: 0.6666 - acc: 0.5900 - val_loss: 0.6541 - val_acc: 0.6238
Epoch 10/1000
2497802/2497802 [==============================] - ETA: 17s - loss: 0.6674 - acc: 0.58 - ETA: 15s - loss: 0.6676 - acc: 0.58 - ETA: 14s - loss: 0.6668 - acc: 0.58 - ETA: 14s - loss: 0.6666 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6670 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6670 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6670 - acc: 0.58 - ETA: 11s - loss: 0.6668 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 9s - loss: 0.6667 - acc: 0.5898 - ETA: 9s - loss: 0.6666 - acc: 0.589 - ETA: 9s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 7s - loss: 0.6668 - acc: 0.589 - ETA: 7s - loss: 0.6668 - acc: 0.589 - ETA: 7s - loss: 0.6668 - acc: 0.589 - ETA: 6s - loss: 0.6668 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 15s 6us/step - loss: 0.6667 - acc: 0.5902 - val_loss: 0.6537 - val_acc: 0.6237
Epoch 11/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6669 - acc: 0.59 - ETA: 14s - loss: 0.6669 - acc: 0.59 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.58 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.58 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6665 - acc: 0.5906 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5903 - val_loss: 0.6538 - val_acc: 0.6238
Epoch 12/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6672 - acc: 0.58 - ETA: 14s - loss: 0.6666 - acc: 0.58 - ETA: 13s - loss: 0.6667 - acc: 0.58 - ETA: 13s - loss: 0.6666 - acc: 0.58 - ETA: 13s - loss: 0.6663 - acc: 0.58 - ETA: 12s - loss: 0.6664 - acc: 0.58 - ETA: 12s - loss: 0.6667 - acc: 0.58 - ETA: 12s - loss: 0.6667 - acc: 0.58 - ETA: 11s - loss: 0.6667 - acc: 0.58 - ETA: 11s - loss: 0.6665 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6669 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 9s - loss: 0.6670 - acc: 0.5892 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6670 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6670 - acc: 0.589 - ETA: 7s - loss: 0.6670 - acc: 0.589 - ETA: 7s - loss: 0.6670 - acc: 0.589 - ETA: 7s - loss: 0.6670 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 6s - loss: 0.6670 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.589 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5900 - val_loss: 0.6539 - val_acc: 0.6238
Epoch 13/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6654 - acc: 0.59 - ETA: 14s - loss: 0.6660 - acc: 0.59 - ETA: 14s - loss: 0.6669 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6666 - acc: 0.58 - ETA: 12s - loss: 0.6665 - acc: 0.58 - ETA: 11s - loss: 0.6665 - acc: 0.58 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5901 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5903 - val_loss: 0.6542 - val_acc: 0.6239
Epoch 14/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6675 - acc: 0.58 - ETA: 14s - loss: 0.6673 - acc: 0.58 - ETA: 14s - loss: 0.6666 - acc: 0.58 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5909 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5904 - val_loss: 0.6543 - val_acc: 0.6238
Epoch 15/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6670 - acc: 0.59 - ETA: 14s - loss: 0.6672 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.59 - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 13s - loss: 0.6671 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6670 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6670 - acc: 0.58 - ETA: 10s - loss: 0.6669 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6669 - acc: 0.58 - ETA: 9s - loss: 0.6668 - acc: 0.5899 - ETA: 9s - loss: 0.6668 - acc: 0.589 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5902 - val_loss: 0.6540 - val_acc: 0.6239
Epoch 16/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6657 - acc: 0.59 - ETA: 14s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6664 - acc: 0.58 - ETA: 13s - loss: 0.6667 - acc: 0.58 - ETA: 13s - loss: 0.6664 - acc: 0.58 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.58 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.58 - ETA: 11s - loss: 0.6666 - acc: 0.58 - ETA: 11s - loss: 0.6666 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6666 - acc: 0.58 - ETA: 10s - loss: 0.6666 - acc: 0.58 - ETA: 9s - loss: 0.6667 - acc: 0.5898 - ETA: 9s - loss: 0.6667 - acc: 0.589 - ETA: 9s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5906 - val_loss: 0.6535 - val_acc: 0.6238
Epoch 17/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6666 - acc: 0.59 - ETA: 14s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6658 - acc: 0.59 - ETA: 12s - loss: 0.6657 - acc: 0.59 - ETA: 11s - loss: 0.6658 - acc: 0.59 - ETA: 11s - loss: 0.6660 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 9s - loss: 0.6662 - acc: 0.5904 - ETA: 9s - loss: 0.6662 - acc: 0.590 - ETA: 9s - loss: 0.6662 - acc: 0.590 - ETA: 9s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.589 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 14s 6us/step - loss: 0.6666 - acc: 0.5902 - val_loss: 0.6539 - val_acc: 0.6239
Epoch 18/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6674 - acc: 0.59 - ETA: 14s - loss: 0.6665 - acc: 0.59 - ETA: 13s - loss: 0.6668 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 12s - loss: 0.6667 - acc: 0.59 - ETA: 11s - loss: 0.6668 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 10s - loss: 0.6670 - acc: 0.59 - ETA: 10s - loss: 0.6670 - acc: 0.59 - ETA: 10s - loss: 0.6669 - acc: 0.59 - ETA: 9s - loss: 0.6668 - acc: 0.5904 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 15s 6us/step - loss: 0.6664 - acc: 0.5907 - val_loss: 0.6539 - val_acc: 0.6242
Epoch 19/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6657 - acc: 0.59 - ETA: 15s - loss: 0.6664 - acc: 0.59 - ETA: 14s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5907 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5903 - val_loss: 0.6541 - val_acc: 0.6241
Epoch 20/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6668 - acc: 0.59 - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5911 - ETA: 9s - loss: 0.6663 - acc: 0.591 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.589 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5903 - val_loss: 0.6538 - val_acc: 0.6240
Epoch 21/1000
2497802/2497802 [==============================] - ETA: 19s - loss: 0.6668 - acc: 0.58 - ETA: 16s - loss: 0.6668 - acc: 0.58 - ETA: 15s - loss: 0.6672 - acc: 0.58 - ETA: 15s - loss: 0.6670 - acc: 0.58 - ETA: 15s - loss: 0.6672 - acc: 0.58 - ETA: 15s - loss: 0.6670 - acc: 0.58 - ETA: 15s - loss: 0.6671 - acc: 0.58 - ETA: 14s - loss: 0.6670 - acc: 0.58 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6664 - acc: 0.5904 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.589 - ETA: 6s - loss: 0.6668 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6667 - acc: 0.589 - ETA: 4s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 16s 6us/step - loss: 0.6666 - acc: 0.5902 - val_loss: 0.6535 - val_acc: 0.6239
Epoch 22/1000
2497802/2497802 [==============================] - ETA: 19s - loss: 0.6686 - acc: 0.58 - ETA: 16s - loss: 0.6673 - acc: 0.59 - ETA: 15s - loss: 0.6667 - acc: 0.59 - ETA: 14s - loss: 0.6666 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6666 - acc: 0.59 - ETA: 13s - loss: 0.6666 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 10s - loss: 0.6669 - acc: 0.59 - ETA: 10s - loss: 0.6669 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 9s - loss: 0.6667 - acc: 0.5903 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 16s 6us/step - loss: 0.6665 - acc: 0.5902 - val_loss: 0.6540 - val_acc: 0.6240
Epoch 23/1000
2497802/2497802 [==============================] - ETA: 20s - loss: 0.6680 - acc: 0.59 - ETA: 16s - loss: 0.6674 - acc: 0.59 - ETA: 14s - loss: 0.6672 - acc: 0.59 - ETA: 13s - loss: 0.6670 - acc: 0.59 - ETA: 13s - loss: 0.6669 - acc: 0.59 - ETA: 13s - loss: 0.6669 - acc: 0.59 - ETA: 12s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6667 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 9s - loss: 0.6669 - acc: 0.5901 - ETA: 9s - loss: 0.6669 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6668 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.589 - ETA: 5s - loss: 0.6666 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 4s - loss: 0.6667 - acc: 0.589 - ETA: 4s - loss: 0.6667 - acc: 0.589 - ETA: 4s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6667 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6667 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - 15s 6us/step - loss: 0.6666 - acc: 0.5899 - val_loss: 0.6541 - val_acc: 0.6241
Epoch 24/1000
2497802/2497802 [==============================] - ETA: 18s - loss: 0.6661 - acc: 0.59 - ETA: 16s - loss: 0.6669 - acc: 0.58 - ETA: 15s - loss: 0.6667 - acc: 0.58 - ETA: 14s - loss: 0.6668 - acc: 0.58 - ETA: 14s - loss: 0.6669 - acc: 0.59 - ETA: 13s - loss: 0.6668 - acc: 0.59 - ETA: 13s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5908 - ETA: 9s - loss: 0.6663 - acc: 0.590 - ETA: 9s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5905 - val_loss: 0.6538 - val_acc: 0.6242
Epoch 25/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6677 - acc: 0.58 - ETA: 14s - loss: 0.6674 - acc: 0.58 - ETA: 14s - loss: 0.6673 - acc: 0.58 - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 12s - loss: 0.6670 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 11s - loss: 0.6670 - acc: 0.58 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.58 - ETA: 11s - loss: 0.6668 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6668 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 9s - loss: 0.6668 - acc: 0.5896 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 9s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5907 - val_loss: 0.6539 - val_acc: 0.6242
Epoch 26/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6686 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6665 - acc: 0.5905 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5901 - val_loss: 0.6540 - val_acc: 0.6240
Epoch 27/1000
2497802/2497802 [==============================] - ETA: 18s - loss: 0.6674 - acc: 0.59 - ETA: 16s - loss: 0.6672 - acc: 0.58 - ETA: 15s - loss: 0.6670 - acc: 0.58 - ETA: 15s - loss: 0.6668 - acc: 0.59 - ETA: 15s - loss: 0.6668 - acc: 0.59 - ETA: 14s - loss: 0.6669 - acc: 0.59 - ETA: 14s - loss: 0.6671 - acc: 0.59 - ETA: 13s - loss: 0.6671 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 9s - loss: 0.6667 - acc: 0.5903 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 17s 7us/step - loss: 0.6666 - acc: 0.5901 - val_loss: 0.6544 - val_acc: 0.6240
Epoch 28/1000
2497802/2497802 [==============================] - ETA: 20s - loss: 0.6648 - acc: 0.59 - ETA: 18s - loss: 0.6662 - acc: 0.59 - ETA: 17s - loss: 0.6665 - acc: 0.59 - ETA: 16s - loss: 0.6659 - acc: 0.59 - ETA: 15s - loss: 0.6660 - acc: 0.59 - ETA: 15s - loss: 0.6661 - acc: 0.59 - ETA: 15s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6664 - acc: 0.59 - ETA: 14s - loss: 0.6663 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.58 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6662 - acc: 0.5902 - ETA: 9s - loss: 0.6662 - acc: 0.590 - ETA: 8s - loss: 0.6662 - acc: 0.590 - ETA: 8s - loss: 0.6662 - acc: 0.590 - ETA: 8s - loss: 0.6662 - acc: 0.590 - ETA: 7s - loss: 0.6662 - acc: 0.590 - ETA: 7s - loss: 0.6662 - acc: 0.590 - ETA: 7s - loss: 0.6662 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 6s - loss: 0.6662 - acc: 0.590 - ETA: 6s - loss: 0.6662 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6663 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 17s 7us/step - loss: 0.6665 - acc: 0.5903 - val_loss: 0.6545 - val_acc: 0.6240
Epoch 29/1000
2497802/2497802 [==============================] - ETA: 20s - loss: 0.6668 - acc: 0.59 - ETA: 17s - loss: 0.6668 - acc: 0.59 - ETA: 16s - loss: 0.6663 - acc: 0.59 - ETA: 16s - loss: 0.6662 - acc: 0.59 - ETA: 15s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6664 - acc: 0.59 - ETA: 14s - loss: 0.6664 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6667 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 9s - loss: 0.6665 - acc: 0.5905 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 16s 6us/step - loss: 0.6665 - acc: 0.5906 - val_loss: 0.6543 - val_acc: 0.6242
Epoch 30/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6671 - acc: 0.58 - ETA: 14s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6666 - acc: 0.58 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.58 - ETA: 10s - loss: 0.6665 - acc: 0.58 - ETA: 10s - loss: 0.6665 - acc: 0.58 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6664 - acc: 0.5901 - ETA: 9s - loss: 0.6663 - acc: 0.590 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6663 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 2s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - 15s 6us/step - loss: 0.6666 - acc: 0.5899 - val_loss: 0.6539 - val_acc: 0.6242
Epoch 31/1000
2497802/2497802 [==============================] - ETA: 17s - loss: 0.6654 - acc: 0.59 - ETA: 15s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6674 - acc: 0.58 - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 13s - loss: 0.6671 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.58 - ETA: 12s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 10s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 9s - loss: 0.6669 - acc: 0.5894 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 9s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.589 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 16s 6us/step - loss: 0.6665 - acc: 0.5904 - val_loss: 0.6543 - val_acc: 0.6244
Epoch 00031: early stopping
Wall time: 7min 54s

<keras.callbacks.History at 0x2689253c2e8>

%%time
# Fit the model
classifier.fit(X_train, y_train, epochs=1000, batch_size=200000,validation_data=(X_val, y_val),callbacks=callbacks2)

Train on 2497802 samples, validate on 624451 samples
Epoch 1/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6664 - acc: 0.5908 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6663 - acc: 0.590 - ETA: 0s - loss: 0.6663 - acc: 0.590 - 17s 7us/step - loss: 0.6664 - acc: 0.5905 - val_loss: 0.6541 - val_acc: 0.6243
Epoch 2/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6666 - acc: 0.58 - ETA: 9s - loss: 0.6665 - acc: 0.5899 - ETA: 8s - loss: 0.6665 - acc: 0.589 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 18s 7us/step - loss: 0.6664 - acc: 0.5902 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 3/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5902 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 16s 6us/step - loss: 0.6666 - acc: 0.5902 - val_loss: 0.6542 - val_acc: 0.6244
Epoch 4/1000
2497802/2497802 [==============================] - ETA: 14s - loss: 0.6656 - acc: 0.59 - ETA: 12s - loss: 0.6658 - acc: 0.59 - ETA: 11s - loss: 0.6661 - acc: 0.59 - ETA: 10s - loss: 0.6660 - acc: 0.59 - ETA: 8s - loss: 0.6661 - acc: 0.5910 - ETA: 7s - loss: 0.6662 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6662 - acc: 0.590 - ETA: 0s - loss: 0.6662 - acc: 0.590 - 15s 6us/step - loss: 0.6662 - acc: 0.5907 - val_loss: 0.6540 - val_acc: 0.6244
Epoch 5/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6661 - acc: 0.59 - ETA: 8s - loss: 0.6665 - acc: 0.5907 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 16s 6us/step - loss: 0.6666 - acc: 0.5904 - val_loss: 0.6544 - val_acc: 0.6243
Epoch 6/1000
2497802/2497802 [==============================] - ETA: 14s - loss: 0.6657 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.58 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 8s - loss: 0.6667 - acc: 0.5899 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 15s 6us/step - loss: 0.6664 - acc: 0.5902 - val_loss: 0.6540 - val_acc: 0.6244
Epoch 7/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5907 - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6662 - acc: 0.591 - ETA: 0s - loss: 0.6662 - acc: 0.591 - 16s 6us/step - loss: 0.6662 - acc: 0.5911 - val_loss: 0.6540 - val_acc: 0.6244
Epoch 8/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6662 - acc: 0.58 - ETA: 12s - loss: 0.6666 - acc: 0.58 - ETA: 11s - loss: 0.6665 - acc: 0.58 - ETA: 9s - loss: 0.6668 - acc: 0.5895 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 15s 6us/step - loss: 0.6667 - acc: 0.5901 - val_loss: 0.6544 - val_acc: 0.6243
Epoch 9/1000
2497802/2497802 [==============================] - ETA: 14s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5907 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6662 - acc: 0.590 - ETA: 2s - loss: 0.6662 - acc: 0.590 - ETA: 1s - loss: 0.6661 - acc: 0.591 - ETA: 0s - loss: 0.6661 - acc: 0.591 - 15s 6us/step - loss: 0.6661 - acc: 0.5910 - val_loss: 0.6539 - val_acc: 0.6243
Epoch 10/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6661 - acc: 0.58 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 11s - loss: 0.6661 - acc: 0.59 - ETA: 10s - loss: 0.6659 - acc: 0.59 - ETA: 9s - loss: 0.6659 - acc: 0.5909 - ETA: 8s - loss: 0.6661 - acc: 0.590 - ETA: 6s - loss: 0.6662 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6662 - acc: 0.590 - ETA: 3s - loss: 0.6662 - acc: 0.590 - ETA: 1s - loss: 0.6662 - acc: 0.590 - ETA: 0s - loss: 0.6662 - acc: 0.590 - 16s 6us/step - loss: 0.6663 - acc: 0.5906 - val_loss: 0.6541 - val_acc: 0.6243
Epoch 11/1000
2497802/2497802 [==============================] - ETA: 14s - loss: 0.6658 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5907 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 17s 7us/step - loss: 0.6665 - acc: 0.5902 - val_loss: 0.6543 - val_acc: 0.6243
Epoch 12/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6661 - acc: 0.59 - ETA: 14s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 9s - loss: 0.6662 - acc: 0.5914 - ETA: 7s - loss: 0.6662 - acc: 0.591 - ETA: 6s - loss: 0.6662 - acc: 0.591 - ETA: 4s - loss: 0.6663 - acc: 0.591 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 18s 7us/step - loss: 0.6664 - acc: 0.5907 - val_loss: 0.6542 - val_acc: 0.6242
Epoch 13/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6664 - acc: 0.59 - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.58 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5906 - ETA: 7s - loss: 0.6663 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 16s 7us/step - loss: 0.6664 - acc: 0.5904 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 14/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6665 - acc: 0.59 - ETA: 14s - loss: 0.6666 - acc: 0.58 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5906 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6663 - acc: 0.590 - ETA: 0s - loss: 0.6663 - acc: 0.590 - 17s 7us/step - loss: 0.6663 - acc: 0.5905 - val_loss: 0.6540 - val_acc: 0.6242
Epoch 15/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6656 - acc: 0.59 - ETA: 14s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5906 - ETA: 7s - loss: 0.6663 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 18s 7us/step - loss: 0.6664 - acc: 0.5906 - val_loss: 0.6543 - val_acc: 0.6242
Epoch 16/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5905 - ETA: 7s - loss: 0.6663 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6662 - acc: 0.590 - ETA: 4s - loss: 0.6662 - acc: 0.590 - ETA: 3s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6663 - acc: 0.590 - ETA: 0s - loss: 0.6663 - acc: 0.590 - 17s 7us/step - loss: 0.6663 - acc: 0.5907 - val_loss: 0.6541 - val_acc: 0.6243
Epoch 17/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6664 - acc: 0.59 - ETA: 14s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6658 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5902 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 18s 7us/step - loss: 0.6664 - acc: 0.5903 - val_loss: 0.6542 - val_acc: 0.6244
Epoch 18/1000
2497802/2497802 [==============================] - ETA: 14s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6665 - acc: 0.5901 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 17s 7us/step - loss: 0.6664 - acc: 0.5906 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 19/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 11s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.58 - ETA: 9s - loss: 0.6666 - acc: 0.5900 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6663 - acc: 0.590 - ETA: 0s - loss: 0.6663 - acc: 0.590 - 16s 6us/step - loss: 0.6664 - acc: 0.5905 - val_loss: 0.6540 - val_acc: 0.6244
Epoch 00019: early stopping
Wall time: 5min 12s

<keras.callbacks.History at 0x26879bcb2b0>

%%time
# Fit the model
classifier.fit(X_train, y_train, epochs=1000, batch_size=1000000,validation_data=(X_val, y_val),callbacks=callbacks2)

Train on 2497802 samples, validate on 624451 samples
Epoch 1/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6662 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.591 - 16s 6us/step - loss: 0.6663 - acc: 0.5910 - val_loss: 0.6540 - val_acc: 0.6244
Epoch 2/1000
2497802/2497802 [==============================] - ETA: 8s - loss: 0.6661 - acc: 0.591 - ETA: 2s - loss: 0.6662 - acc: 0.590 - 15s 6us/step - loss: 0.6663 - acc: 0.5909 - val_loss: 0.6541 - val_acc: 0.6243
Epoch 3/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6665 - acc: 0.589 - ETA: 2s - loss: 0.6664 - acc: 0.590 - 16s 6us/step - loss: 0.6664 - acc: 0.5904 - val_loss: 0.6541 - val_acc: 0.6242
Epoch 4/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6662 - acc: 0.591 - ETA: 3s - loss: 0.6662 - acc: 0.590 - 16s 7us/step - loss: 0.6663 - acc: 0.5906 - val_loss: 0.6542 - val_acc: 0.6242
Epoch 5/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - 16s 6us/step - loss: 0.6664 - acc: 0.5903 - val_loss: 0.6542 - val_acc: 0.6242
Epoch 6/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6661 - acc: 0.590 - ETA: 3s - loss: 0.6663 - acc: 0.590 - 16s 6us/step - loss: 0.6664 - acc: 0.5906 - val_loss: 0.6542 - val_acc: 0.6243
Epoch 7/1000
2497802/2497802 [==============================] - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6664 - acc: 0.5906 - val_loss: 0.6542 - val_acc: 0.6244
Epoch 8/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6663 - acc: 0.591 - ETA: 2s - loss: 0.6663 - acc: 0.590 - 15s 6us/step - loss: 0.6663 - acc: 0.5906 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 9/1000
2497802/2497802 [==============================] - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5905 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 10/1000
2497802/2497802 [==============================] - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6664 - acc: 0.5901 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 11/1000
2497802/2497802 [==============================] - ETA: 8s - loss: 0.6659 - acc: 0.591 - ETA: 2s - loss: 0.6661 - acc: 0.590 - 16s 6us/step - loss: 0.6661 - acc: 0.5908 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 00011: early stopping
Wall time: 2min 51s

<keras.callbacks.History at 0x267859aaa90>

# evaluate the model
scores = classifier.evaluate(X_val, y_val)
print("\n%s: %.2f%%" % (classifier.metrics_names[1], scores[1]*100))

624451/624451 [==============================] - ETA: 2: - ETA: 9s - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - 7s 11us/step

acc: 62.44%

%%time
predictions=classifier.predict(X_test)

Wall time: 11.3 s

%%time
#predictions_scores=model.predict_proba(X_test)

Wall time: 0 ns

preds=[1 if x>=0.5 else 0 for x in predictions]

confusion_matrix(y_test,preds)

array([[424152, 244775],
       [259915, 409267]], dtype=int64)

accuracy_score(y_test,preds)

0.6228334164107707

recall_score(y_test,preds)

0.6115929597628149

precision_score(y_test,preds)

0.6257503340764049

roc_auc_score(y_test,predictions)

0.6730229533169875

predictions=classifier.predict(df_test[important_columns])

solution['HasDetections']=predictions

solution.to_csv('submit_31st.csv',index=False)

from sklearn.ensemble import AdaBoostClassifier,VotingClassifier

C:\Users\gandh\Anaconda3\lib\site-packages\sklearn\ensemble\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
  from numpy.core.umath_tests import inner1d

clf1=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='split', learning_rate=0.15, max_depth=15,
        min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=1000, n_jobs=-1, num_leaves=200, objective=None,
        random_state=42, reg_alpha=0.5, reg_lambda=0.0, silent=False,
        subsample=0.9, subsample_for_bin=200000, subsample_freq=5)

clf2=AdaBoostClassifier(learning_rate=0.1,n_estimators=100,random_state=42)

vc=VotingClassifier(estimators=[('lgb',clf1),('adb',clf2)],voting='soft',weights=[2,1])

import gc
gc.collect()

0

%%time
#vc.fit(X_train[important_columns_01],y_train)

Wall time: 0 ns

%%time
clf2.fit(X_train,y_train)

Wall time: 31min 47s

AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
          learning_rate=0.1, n_estimators=100, random_state=42)

predictions=clf2.predict(X_test)
prediction_scores=clf2.predict_proba(X_test)
scores=[]
for score in prediction_scores:
    scores.append(score[1])

print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,scores))

Confusion Matrix for the Model : 
 [[368338 300589]
 [212696 456486]]
Accuracy of the model :  0.6164101728633468
Precision of the Model :  0.6029600766106397
Recall score of the Model :  0.6821552283235353
Area under ROC curve for the Model :  0.6695695166372357

# Cross-validation
from sklearn.model_selection import KFold, StratifiedKFold, KFold #for K-fold cross validation
from sklearn.model_selection import cross_val_score #score evaluation
from sklearn.model_selection import cross_val_predict #prediction
from sklearn.model_selection import cross_validate

import lightgbm as lgb

#kf = StratifiedKFold(random_state=42,shuffle=False,n_splits=5)

clf=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='split', learning_rate=0.15, max_depth=-1,
        min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=1000, n_jobs=-1, objective=None,
        random_state=42, reg_alpha=0.7, reg_lambda=0.7, silent=False,
        subsample=0.9,subsample_freq=5)

param = {'objective':'binary',
         'boosting': 'gbdt',     
         'learning_rate': 0.05,
         'max_depth': -1,
         'num_leaves': 100,
         'sub_feature': 0.9,
         'sub_row':0.9,
         'lambda_l1': 0.6,
         'lambda_l2': 0.6,
         "random_state": 133,
         "verbosity": -1}

features=X.columns.tolist()
import gc

Stratified 5 fold cross validation referred from https://www.kaggle.com/roydatascience/light-gbm-on-stratified-k-folds-malwares

%%time
max_iter = 5
#categorical_columns = [c for c in categorical_columns if c not in ['MachineIdentifier']]
#features = [c for c in train.columns if c not in ['MachineIdentifier']]
gc.collect()

folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=15)
oof = np.zeros(len(X))
predictions = np.zeros(len(df_test))
feature_importance_df = pd.DataFrame()
score = [0 for _ in range(folds.n_splits)]

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X,y.values)):
    print("fold {}".format(fold_))
    trn_data = lgb.Dataset(X.iloc[trn_idx][features], label=y.iloc[trn_idx])
    val_data = lgb.Dataset(X.iloc[val_idx][features], label=y.iloc[val_idx])

    num_round = 1000
    clf = lgb.train(param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=-1, early_stopping_rounds = 200)
    oof[val_idx] = clf.predict(X.iloc[val_idx][features], num_iteration=clf.best_iteration)
    
    fold_importance_df = pd.DataFrame()
    fold_importance_df["feature"] = features
    fold_importance_df["importance"] = clf.feature_importance(importance_type='gain')
    fold_importance_df["fold"] = fold_ + 1
    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
    
    # we perform predictions by chunks
    initial_idx = 0
    chunk_size = 1000000
    current_pred = np.zeros(len(df_test))
    
    while initial_idx < df_test.shape[0]:
        final_idx = min(initial_idx + chunk_size, df_test.shape[0])
        idx = range(initial_idx, final_idx)
        current_pred[idx] = clf.predict(df_test.iloc[idx][features], num_iteration=clf.best_iteration)
        initial_idx = final_idx
    
    predictions += current_pred / min(folds.n_splits, max_iter)
    score[fold_] = roc_auc_score(y.iloc[val_idx], oof[val_idx])
    if fold_ == max_iter - 1: break
        
if (folds.n_splits == max_iter):
    print("CV score: {:<8.5f}".format(roc_auc_score(y, oof)))
else:
     print("CV score: {:<8.5f}".format(sum(score) / max_iter))

fold 0
Training until validation scores don't improve for 200 rounds.
Did not meet early stopping. Best iteration is:
[1000]	training's binary_logloss: 0.596156	valid_1's binary_logloss: 0.604861
fold 1
Training until validation scores don't improve for 200 rounds.
Did not meet early stopping. Best iteration is:
[1000]	training's binary_logloss: 0.595986	valid_1's binary_logloss: 0.60519
fold 2
Training until validation scores don't improve for 200 rounds.
Did not meet early stopping. Best iteration is:
[1000]	training's binary_logloss: 0.596275	valid_1's binary_logloss: 0.605196
fold 3
Training until validation scores don't improve for 200 rounds.
Did not meet early stopping. Best iteration is:
[1000]	training's binary_logloss: 0.596253	valid_1's binary_logloss: 0.604526
fold 4
Training until validation scores don't improve for 200 rounds.
Did not meet early stopping. Best iteration is:
[1000]	training's binary_logloss: 0.59619	valid_1's binary_logloss: 0.604496
CV score: 0.72831 
Wall time: 1h 35min 17s

cols = (feature_importance_df[["feature", "importance"]]
        .groupby("feature")
        .mean()
        .sort_values(by="importance", ascending=False)[:1000].index)

best_features = feature_importance_df.loc[feature_importance_df.feature.isin(cols)]

plt.figure(figsize=(14,25))
sns.barplot(x="importance",
            y="feature",
            data=best_features.sort_values(by="importance",
                                           ascending=False))
plt.title('LightGBM Features (avg over folds)')
plt.tight_layout()
plt.savefig('lgbm_importances2.png')

#solution=pd.DataFrame()

#solution['MachineIdentifier']=df_test['MachineIdentifier']

solution['HasDetections']=predictions

solution.to_csv('submit_31st.csv',index=False)

	count	mean	std	min	25%	50%	75%	max
IsBeta	8921483.0	7.509962e-06	2.740421e-03	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
RtpStateBitfield	8889165.0	NaN	0.000000e+00	0.000000	7.000000	7.0	7.000000e+00	3.500000e+01
IsSxsPassiveMode	8921483.0	1.733378e-02	1.305118e-01	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
DefaultBrowsersIdentifier	433438.0	NaN	NaN	1.000000	788.000000	1632.0	2.372000e+03	3.212000e+03
AVProductStatesIdentifier	8885262.0	4.948320e+04	1.379994e+04	3.000000	49480.000000	53447.0	5.344700e+04	7.050700e+04
AVProductsInstalled	8885262.0	NaN	0.000000e+00	0.000000	1.000000	1.0	2.000000e+00	7.000000e+00
AVProductsEnabled	8885262.0	NaN	0.000000e+00	0.000000	1.000000	1.0	1.000000e+00	5.000000e+00
HasTpm	8921483.0	9.879711e-01	1.090149e-01	0.000000	1.000000	1.0	1.000000e+00	1.000000e+00
CountryIdentifier	8921483.0	1.080490e+02	6.304706e+01	1.000000	51.000000	97.0	1.620000e+02	2.220000e+02
CityIdentifier	8596074.0	8.049152e+04	4.873461e+04	5.000000	36825.000000	82373.0	1.237000e+05	1.679620e+05
OrganizationIdentifier	6169965.0	NaN	0.000000e+00	1.000000	18.000000	27.0	2.700000e+01	5.200000e+01
GeoNameIdentifier	8921270.0	NaN	NaN	1.000000	89.000000	181.0	2.670000e+02	2.960000e+02
LocaleEnglishNameIdentifier	8921483.0	1.228161e+02	6.932125e+01	1.000000	74.000000	88.0	1.820000e+02	2.830000e+02
OsBuild	8921483.0	1.571997e+04	2.190685e+03	7600.000000	15063.000000	16299.0	1.713400e+04	1.824400e+04
OsSuite	8921483.0	5.751534e+02	2.480847e+02	16.000000	256.000000	768.0	7.680000e+02	7.840000e+02
IsProtected	8885439.0	NaN	0.000000e+00	0.000000	1.000000	1.0	1.000000e+00	1.000000e+00
AutoSampleOptIn	8921483.0	2.891896e-05	5.377558e-03	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
SMode	8383724.0	4.649162e-04	2.104187e-02	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
IeVerIdentifier	8862589.0	NaN	NaN	1.000000	111.000000	117.0	1.370000e+02	4.290000e+02
Firewall	8830133.0	NaN	0.000000e+00	0.000000	1.000000	1.0	1.000000e+00	1.000000e+00
UacLuaenable	8910645.0	1.216142e+01	9.867765e+03	0.000000	1.000000	1.0	1.000000e+00	1.677722e+07
Census_OEMNameIdentifier	8826005.0	NaN	NaN	1.000000	1443.000000	2102.0	2.668000e+03	6.144000e+03
Census_OEMModelIdentifier	8819250.0	2.378578e+05	7.148446e+04	1.000000	189692.000000	247458.0	3.044180e+05	3.454980e+05
Census_ProcessorCoreCount	8880177.0	NaN	0.000000e+00	1.000000	2.000000	4.0	4.000000e+00	1.920000e+02
Census_ProcessorManufacturerIdentifier	8880170.0	NaN	0.000000e+00	1.000000	5.000000	5.0	5.000000e+00	1.000000e+01
Census_ProcessorModelIdentifier	8880140.0	NaN	NaN	2.000000	1998.000000	2500.0	2.874000e+03	4.480000e+03
Census_PrimaryDiskTotalCapacity	8868467.0	2.912138e+06	4.451633e+09	0.000000	239372.000000	476940.0	9.538690e+05	8.160437e+12
Census_SystemVolumeTotalCapacity	8868481.0	3.823069e+05	3.233614e+05	0.000000	120775.000000	249500.0	4.759730e+05	4.768710e+07
Census_HasOpticalDiskDrive	8921483.0	7.718728e-02	2.668884e-01	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
Census_TotalPhysicalRAM	8840950.0	6.109390e+03	4.971148e+03	255.000000	4096.000000	4096.0	8.192000e+03	1.572864e+06
Census_InternalPrimaryDiagonalDisplaySizeInInches	8874349.0	NaN	0.000000e+00	0.700195	13.898438	15.5	1.720312e+01	1.822500e+02
Census_InternalPrimaryDisplayResolutionHorizontal	8874497.0	NaN	NaN	-1.000000	1366.000000	1366.0	1.920000e+03	1.228800e+04
Census_InternalPrimaryDisplayResolutionVertical	8874497.0	NaN	NaN	-1.000000	768.000000	768.0	1.080000e+03	8.640000e+03
Census_InternalBatteryNumberOfCharges	8652728.0	1.123782e+09	1.933305e+09	0.000000	0.000000	0.0	4.294967e+09	4.294967e+09
Census_OSBuildNumber	8921483.0	1.583483e+04	1.961743e+03	7600.000000	15063.000000	16299.0	1.713400e+04	1.824400e+04
Census_OSBuildRevision	8921483.0	9.730490e+02	2.931971e+03	0.000000	167.000000	285.0	5.470000e+02	4.173600e+04
Census_OSInstallLanguageIdentifier	8861399.0	NaN	0.000000e+00	1.000000	8.000000	9.0	2.000000e+01	3.900000e+01
Census_OSUILocaleIdentifier	8921483.0	6.046534e+01	4.499992e+01	1.000000	31.000000	34.0	9.000000e+01	1.620000e+02
Census_IsPortableOperatingSystem	8921483.0	5.452008e-04	2.334317e-02	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
Census_IsFlightingInternal	1512724.0	1.388788e-05	3.726959e-03	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
Census_IsFlightsDisabled	8760960.0	1.007318e-05	3.173828e-03	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
Census_ThresholdOptIn	3254158.0	2.508163e-04	1.582336e-02	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
Census_FirmwareManufacturerIdentifier	8738226.0	NaN	NaN	2.000000	142.000000	500.0	5.560000e+02	1.092000e+03
Census_FirmwareVersionIdentifier	8761350.0	3.268055e+04	2.112612e+04	3.000000	13156.000000	33070.0	5.243600e+04	7.210500e+04
Census_IsSecureBootEnabled	8921483.0	4.860229e-01	4.998046e-01	0.000000	0.000000	0.0	1.000000e+00	1.000000e+00
Census_IsWIMBootEnabled	3261780.0	2.980232e-07	5.459785e-04	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
Census_IsVirtualDevice	8905530.0	7.202148e-03	8.453369e-02	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
Census_IsTouchEnabled	8921483.0	1.255431e-01	3.313338e-01	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
Census_IsPenCapable	8921483.0	3.807091e-02	1.913675e-01	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
Census_IsAlwaysOnAlwaysConnectedCapable	8850140.0	NaN	0.000000e+00	0.000000	0.000000	0.0	0.000000e+00	1.000000e+00
Wdft_IsGamer	8618032.0	NaN	0.000000e+00	0.000000	0.000000	0.0	1.000000e+00	1.000000e+00
Wdft_RegionIdentifier	8618032.0	NaN	0.000000e+00	1.000000	3.000000	10.0	1.100000e+01	1.500000e+01
HasDetections	8921483.0	4.997927e-01	5.000000e-01	0.000000	0.000000	0.0	1.000000e+00	1.000000e+00

	count	mean	std	min	25%	50%	75%	max
Census_ProcessorCoreCount	8920724.0	NaN	0.000000e+00	1.000000	2.000000	4.0	4.000000e+00	1.920000e+02
Census_PrimaryDiskTotalCapacity	8920724.0	2.895155e+06	4.438576e+09	0.000000	244197.000000	476940.0	9.538690e+05	8.160437e+12
Census_SystemVolumeTotalCapacity	8920724.0	3.816120e+05	3.230770e+05	0.000000	120827.000000	249500.0	4.759650e+05	4.768710e+07
Census_TotalPhysicalRAM	8920724.0	6.091263e+03	4.951270e+03	255.000000	4096.000000	4096.0	8.192000e+03	1.572864e+06
Census_InternalPrimaryDiagonalDisplaySizeInInches	8920724.0	NaN	0.000000e+00	0.700195	13.898438	15.5	1.720312e+01	1.822500e+02
Census_InternalPrimaryDisplayResolutionHorizontal	8920724.0	NaN	NaN	-1.000000	1366.000000	1366.0	1.920000e+03	1.228800e+04
Census_InternalPrimaryDisplayResolutionVertical	8920724.0	NaN	NaN	-1.000000	768.000000	768.0	1.080000e+03	8.640000e+03
Census_InternalBatteryNumberOfCharges	8920724.0	1.089876e+09	1.925254e+09	0.000000	0.000000	0.0	4.294967e+09	4.294967e+09

	OsPlatformSubRelease	AVProductStatesIdentifier	RtpStateBitfield	Census_DeviceFamily	Wdft_IsGamer	AVProductsEnabled	HasDetections
77402	rs4	53447.0	7.0	Windows.Desktop	0.0	1.0	0
282900	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
359909	rs1	53447.0	7.0	Windows.Desktop	0.0	1.0	0
423049	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
843490	rs4	53447.0	7.0	Windows.Desktop	0.0	1.0	1
844448	rs4	53447.0	7.0	Windows.Desktop	0.0	1.0	0
912233	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
942736	rs1	53447.0	7.0	Windows.Server	0.0	1.0	1
1119826	rs4	35379.0	0.0	Windows.Desktop	0.0	1.0	0
1827338	rs1	53447.0	7.0	Windows.Server	0.0	1.0	1
1866798	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
1882995	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
1905786	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
2382892	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
2404851	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
3168236	rs4	53447.0	7.0	Windows.Desktop	0.0	1.0	1
3283213	rs1	53447.0	7.0	Windows.Server	0.0	1.0	1
3497061	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
3528025	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
3912495	rs4	53447.0	7.0	Windows.Desktop	0.0	1.0	0
3991746	rs1	53447.0	7.0	Windows.Server	0.0	1.0	1
4222767	rs4	53447.0	7.0	Windows.Desktop	0.0	1.0	0
4683372	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
4778357	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
4904555	rs4	53447.0	7.0	Windows.Desktop	1.0	1.0	1
4978744	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
5684683	rs3	53447.0	7.0	Windows.Desktop	0.0	1.0	0
6164464	rs4	53447.0	7.0	Windows.Desktop	0.0	1.0	0
6643797	rs1	53447.0	7.0	Windows.Server	0.0	1.0	0
7898158	rs4	30961.0	7.0	Windows.Desktop	0.0	1.0	0
7957693	rs3	53447.0	7.0	Windows.Desktop	0.0	1.0	0
8564928	rs4	53447.0	7.0	Windows.Desktop	0.0	1.0	1

	Census_ProcessorCoreCount	Census_PrimaryDiskTotalCapacity	Census_SystemVolumeTotalCapacity	Census_TotalPhysicalRAM	Census_InternalPrimaryDiagonalDisplaySizeInInches	Census_InternalPrimaryDisplayResolutionHorizontal	Census_InternalPrimaryDisplayResolutionVertical	Census_InternalBatteryNumberOfCharges
Census_ProcessorCoreCount	1.000000	-0.000187	0.081713	0.595529	0.183298	0.344943	0.315479	0.117078
Census_PrimaryDiskTotalCapacity	-0.000187	1.000000	0.000787	-0.000066	-0.000059	-0.000178	-0.000250	-0.000334
Census_SystemVolumeTotalCapacity	0.081713	0.000787	1.000000	0.153180	0.081792	0.019730	-0.033649	-0.001289
Census_TotalPhysicalRAM	0.595529	-0.000066	0.153180	1.000000	0.241174	0.366244	0.336088	0.182585
Census_InternalPrimaryDiagonalDisplaySizeInInches	0.183298	-0.000059	0.081792	0.241174	1.000000	0.320344	0.277455	0.513360
Census_InternalPrimaryDisplayResolutionHorizontal	0.344943	-0.000178	0.019730	0.366244	0.320344	1.000000	0.901674	0.169044
Census_InternalPrimaryDisplayResolutionVertical	0.315479	-0.000250	-0.033649	0.336088	0.277455	0.901674	1.000000	0.230196
Census_InternalBatteryNumberOfCharges	0.117078	-0.000334	-0.001289	0.182585	0.513360	0.169044	0.230196	1.000000

	Census_ProcessorCoreCount	Census_PrimaryDiskTotalCapacity	Census_SystemVolumeTotalCapacity	Census_TotalPhysicalRAM	Census_InternalPrimaryDiagonalDisplaySizeInInches	Census_InternalPrimaryDisplayResolutionHorizontal	Census_InternalPrimaryDisplayResolutionVertical	Census_InternalBatteryNumberOfCharges
1072023	8.0	238475.0	237428.0	32768.0	54.59375	12288.0	2160.0	4.294967e+09
1252344	12.0	476940.0	476389.0	16384.0	46.00000	12288.0	2160.0	4.294967e+09
3152519	4.0	114473.0	113661.0	16384.0	47.40625	12288.0	2160.0	4.294967e+09

	MachineIdentifier	ProductName	EngineVersion	AppVersion	AvSigVersion	RtpStateBitfield	DefaultBrowsersIdentifier	AVProductStatesIdentifier	...	Census_FirmwareVersionIdentifier	Census_IsWIMBootEnabled	Wdft_RegionIdentifier	HasDetections
0	0000028988387b115f69f31a3bf04f09	win8defender	1.1.15100.1	4.18.1807.18075	1.273.1735.0	7.0	NaN	53447.0	...	36144.0	NaN	10.0	0
1	000007535c3f730efa9ea0b7ef1bd645	win8defender	1.1.14600.4	4.13.17134.1	1.263.48.0	7.0	NaN	53447.0	...	57858.0	NaN	8.0	0
2	000007905a28d863f6d0d597892cd692	win8defender	1.1.15100.1	4.18.1807.18075	1.273.1341.0	7.0	NaN	53447.0	...	52682.0	NaN	3.0	0
3	00000b11598a75ea8ba1beea8459149f	win8defender	1.1.15100.1	4.18.1807.18075	1.273.1527.0	7.0	NaN	53447.0	...	20050.0	NaN	3.0	1
4	000014a5f00daa18e76b81417eeb99fc	win8defender	1.1.15100.1	4.18.1807.18075	1.273.1379.0	7.0	NaN	53447.0	...	19844.0	0.0	1.0	1

	Census_ProcessorCoreCount	Census_PrimaryDiskTotalCapacity	Census_SystemVolumeTotalCapacity	Census_TotalPhysicalRAM	Census_InternalPrimaryDiagonalDisplaySizeInInches	Census_InternalPrimaryDisplayResolutionHorizontal	Census_InternalPrimaryDisplayResolutionVertical	Census_InternalBatteryNumberOfCharges
86641	4.0	953869.0	911955.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
174431	4.0	953869.0	910716.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
245597	4.0	953869.0	912060.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
340295	2.0	476940.0	456362.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
403759	4.0	476940.0	51200.0	2048.0	15.500000	-1.0	-1.0	0.000000e+00
439181	4.0	953869.0	912402.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
540172	8.0	953869.0	939567.0	8192.0	15.500000	-1.0	-1.0	4.294967e+09
627565	6.0	953869.0	99019.0	8192.0	15.500000	-1.0	-1.0	4.294967e+09
695468	4.0	953869.0	911197.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
1003210	4.0	953869.0	911997.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
1133025	4.0	476940.0	433904.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
1138140	4.0	29600.0	28920.0	1024.0	15.500000	-1.0	-1.0	1.400000e+01
1170739	4.0	476940.0	435167.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
1173099	4.0	476940.0	433682.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
1190003	8.0	953869.0	912728.0	8192.0	15.500000	-1.0	-1.0	0.000000e+00
1240120	2.0	152627.0	101949.0	3072.0	15.500000	-1.0	-1.0	4.294967e+09
1259038	4.0	953869.0	910677.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
1301984	4.0	122104.0	106620.0	4096.0	11.601562	-1.0	-1.0	0.000000e+00
1304544	2.0	476940.0	466938.0	2048.0	15.500000	-1.0	-1.0	4.294967e+09
1349133	4.0	244198.0	102401.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
1411395	4.0	29600.0	28482.0	2048.0	15.500000	-1.0	-1.0	1.900000e+01
1545770	2.0	122104.0	105636.0	8192.0	15.500000	-1.0	-1.0	0.000000e+00
1578287	4.0	122104.0	120963.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
1648906	4.0	122104.0	97721.0	8192.0	15.500000	-1.0	-1.0	0.000000e+00
1654681	4.0	953869.0	461862.0	6144.0	13.898438	-1.0	-1.0	0.000000e+00
1792269	8.0	114473.0	113907.0	16384.0	15.500000	-1.0	-1.0	4.294967e+09
1797716	8.0	122104.0	61440.0	8192.0	15.500000	-1.0	-1.0	0.000000e+00
1814557	4.0	476940.0	460857.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
1835472	4.0	476940.0	435687.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
1838169	4.0	953869.0	476159.0	8192.0	15.500000	-1.0	-1.0	4.294967e+09
...	...	...	...	...	...	...	...	...
7009604	4.0	953869.0	907644.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
7058425	2.0	476940.0	435799.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
7103069	2.0	305245.0	267829.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
7143810	4.0	953869.0	913225.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
7174106	2.0	953869.0	81921.0	4096.0	15.500000	-1.0	-1.0	4.294967e+09
7311245	2.0	476940.0	462394.0	4096.0	15.500000	-1.0	-1.0	8.300000e+01
7509075	2.0	305245.0	104448.0	2048.0	15.500000	-1.0	-1.0	4.294967e+09
7598414	4.0	122104.0	120817.0	4096.0	15.500000	-1.0	-1.0	2.800000e+01
7637536	2.0	305245.0	304693.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
7801589	4.0	228936.0	123000.0	8192.0	15.500000	-1.0	-1.0	4.294967e+09
7841312	2.0	476940.0	460562.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
7841507	4.0	953869.0	184207.0	8192.0	15.500000	-1.0	-1.0	4.294967e+09
7926866	4.0	715404.0	691297.0	6144.0	15.500000	-1.0	-1.0	0.000000e+00
7976558	4.0	953869.0	380358.0	4096.0	15.500000	-1.0	-1.0	1.250000e+02
8038076	4.0	953869.0	912986.0	6144.0	15.500000	-1.0	-1.0	0.000000e+00
8100681	2.0	305245.0	289783.0	2048.0	15.500000	-1.0	-1.0	0.000000e+00
8187745	2.0	476940.0	461258.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
8247734	6.0	953869.0	249025.0	8192.0	15.500000	-1.0	-1.0	4.294967e+09
8273003	8.0	122104.0	121027.0	4096.0	15.500000	-1.0	-1.0	3.300000e+01
8380921	2.0	305245.0	99495.0	2048.0	15.500000	-1.0	-1.0	4.294967e+09
8381285	4.0	29600.0	28920.0	1024.0	15.500000	-1.0	-1.0	9.000000e+00
8430613	2.0	152587.0	151614.0	2048.0	15.500000	-1.0	-1.0	4.294967e+09
8463891	2.0	953869.0	285672.0	4096.0	15.500000	-1.0	-1.0	4.294967e+09
8476044	4.0	953869.0	234246.0	8192.0	15.500000	-1.0	-1.0	0.000000e+00
8528644	2.0	114473.0	113920.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
8563916	4.0	953869.0	910611.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
8653632	4.0	476940.0	435447.0	4096.0	23.000000	-1.0	-1.0	0.000000e+00
8694644	4.0	953869.0	912461.0	4096.0	15.500000	-1.0	-1.0	0.000000e+00
8741691	4.0	22902.0	22335.0	6144.0	15.500000	-1.0	-1.0	0.000000e+00
8759520	2.0	114473.0	113500.0	4096.0	15.500000	-1.0	-1.0	4.294967e+09

	MachineIdentifier	EngineVersion	AppVersion	AvSigVersion	AVProductsInstalled	...	Device_PossibleOwnership	AV_mediumrisk	Interaction_01	Interaction_02	Interaction_03	PPI
0	0000028988387b115f69f31a3bf04f09	0	0.0	0	1.0	...	0	1	1024.0	177489.0	1296000.0	89.8125
1	000007535c3f730efa9ea0b7ef1bd645	1	5.0	1	1.0	...	0	1	1024.0	374555.0	1049088.0	112.7500
2	000007905a28d863f6d0d597892cd692	0	0.0	2	1.0	...	0	1	1024.0	566.0	2073600.0	102.4375
3	00000b11598a75ea8ba1beea8459149f	0	0.0	3	1.0	...	1	1	1024.0	11359.0	1049088.0	84.6875
4	000014a5f00daa18e76b81417eeb99fc	0	0.0	4	1.0	...	0	1	1536.0	375040.0	1049088.0	111.9375

	MachineIdentifier	EngineVersion	AppVersion	AvSigVersion	AVProductStatesIdentifier	AVProductsInstalled	...	AV_mediumrisk	Interaction_01	Interaction_02	Interaction_03	PPI
0	0000010489e3af074adeac69c53e555e	-1	2.0	-1	0	1.0	...	1	2048.0	365207.0	2073600.0	142.1250
1	00000176ac758d54827acd545b6315a5	-1	1.0	-1	0	1.0	...	1	2048.0	25377.0	1049088.0	101.1250
2	0000019dcefc128c2d4387c1273dae1d	3	1.0	-1	14	2.0	...	0	2048.0	1142.0	921600.0	105.6875
3	0000055553dc51b1295785415f1a224d	-1	2.0	-1	79	2.0	...	0	2048.0	37595.0	1049088.0	111.9375
4	00000574cefffeca83ec8adf9285b2bf	-1	1.0	-1	0	1.0	...	1	512.0	15434.0	1049088.0	101.1250

	Census_OSBuildRevision	Census_OSSkuName
0	0.032027	-0.018806
1	-0.610708	0.797394
2	-0.707118	-0.018806
3	-0.674982	2.429792
4	-0.578571	-0.018806

	MachineIdentifier	HasDetections
0	0000010489e3af074adeac69c53e555e	0.599641
1	00000176ac758d54827acd545b6315a5	0.748345
2	0000019dcefc128c2d4387c1273dae1d	0.454333
3	0000055553dc51b1295785415f1a224d	0.469111
4	00000574cefffeca83ec8adf9285b2bf	0.662187

	features	importance
108	SmartScreen	402944.766618
107	AVProductStatesIdentifier	280888.551537
106	EngineVersion	89252.064796
105	AppVersion	80002.517550
104	CountryIdentifier	54711.030009
103	Interaction_02	50108.271050
102	Census_OSInstallTypeName	49610.878160
101	Device_PossibleOwnership	47137.113151
100	Census_OSVersion	38533.994658
99	Wdft_IsGamer	37674.048997
98	PPI	31586.841548
97	Wdft_RegionIdentifier	30702.778116
96	Census_ProcessorModelIdentifier	30316.556373
95	CityIdentifier	29678.426420
94	Interaction_03	27916.432337
93	LocaleEnglishNameIdentifier	23723.240506
92	Census_FirmwareVersionIdentifier	23687.421844
91	Census_IsAlwaysOnAlwaysConnectedCapable	23577.176166
90	Census_OSInstallLanguageIdentifier	23491.129490
89	Census_OEMModelIdentifier	23072.982218
88	OsBuildLab	22961.664022
87	Census_ActivationChannel	22429.254086
86	Census_IsVirtualDevice	21808.445312
85	GeoNameIdentifier	19510.442236
84	Census_OEMNameIdentifier	18945.871079
83	Census_OSUILocaleIdentifier	17960.874937
82	IeVerIdentifier	16503.889473
81	RtpStateBitfield	16277.195962
80	Processor	16081.757362
79	Census_OSEdition	15835.934268
...	...	...
29	Interaction_MYYear_16Month_5	163.782850
28	Interaction_MYYear_17Month_4	124.332320
27	Interaction_MYYear_17Month_12	120.278489
26	Interaction_MYYear_16Month_10	102.264990
25	Interaction_MYYear_18Month_2	95.210730
24	Interaction_MYYear_16Month_2	90.008140
23	Interaction_MYYear_16Month_9	87.078200
22	Interaction_MYYear_16Month_4	85.361409
21	Interaction_MYYear_16Month_8	79.674981
20	Interaction_MYYear_17Month_11	62.202849
19	Interaction_MYYear_15Month_11	45.983190
18	Interaction_MYYear_17Month_10	39.600840
17	Interaction_MYYear_16Month_3	30.422991
16	Interaction_MYYear_18Month_9	29.952830
15	Interaction_MYYear_16Month_1	19.081050
14	Interaction_MYYear_17Month_1	7.404160
13	Interaction_MYYear_15Month_3	6.556640
12	Interaction_MYYear_17Month_5	6.225560
11	Interaction_MYYear_17Month_8	5.517140
3	Interaction_MYYear_14Month_7	0.000000
4	Interaction_MYYear_17Month_2	0.000000
6	Interaction_MYYear_14Month_10	0.000000
2	Interaction_MYYear_14Month_3	0.000000
5	Interaction_MYYear_18Month_1	0.000000
1	AV_highrisk	0.000000
7	Interaction_MYYear_15Month_9	0.000000
8	Interaction_MYYear_15Month_8	0.000000
9	Interaction_MYYear_15Month_1	0.000000
10	Census_DeviceFamily	0.000000
0	Census_ThresholdOptIn	0.000000

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	ProductName	EngineVersion	AppVersion	RtpStateBitfield	IsSxsPassiveMode	AVProductStatesIdentifier	HasTpm	CountryIdentifier	CityIdentifier	OrganizationIdentifier	GeoNameIdentifier	LocaleEnglishNameIdentifier	Platform	Processor	OsVer	OsBuild	OsSuite	OsPlatformSubRelease	OsBuildLab	SkuEdition	IsProtected	SMode	IeVerIdentifier	SmartScreen	Firewall	UacLuaenable	Census_MDC2FormFactor	Census_DeviceFamily	Census_OEMNameIdentifier	Census_OEMModelIdentifier	Census_ProcessorManufacturerIdentifier	Census_ProcessorModelIdentifier	Census_PrimaryDiskTypeName	Census_HasOpticalDiskDrive	Census_ChassisTypeName	Census_PowerPlatformRoleName	Census_OSVersion	Census_OSArchitecture	Census_OSBranch	Census_OSBuildNumber	Census_OSBuildRevision	Census_OSEdition	Census_OSSkuName	Census_OSInstallTypeName	Census_OSInstallLanguageIdentifier	Census_OSUILocaleIdentifier	Census_OSWUAutoUpdateOptionsName	Census_GenuineStateName	Census_ActivationChannel	Census_FlightRing	Census_ThresholdOptIn	Census_FirmwareManufacturerIdentifier	Census_FirmwareVersionIdentifier	Census_IsSecureBootEnabled	Census_IsWIMBootEnabled	Census_IsVirtualDevice	Census_IsTouchEnabled	Census_IsPenCapable	Census_IsAlwaysOnAlwaysConnectedCapable	Wdft_IsGamer	Wdft_RegionIdentifier	Interaction_MYYear_14Month_3	Interaction_MYYear_14Month_7	Interaction_MYYear_14Month_10	Interaction_MYYear_15Month_1	Interaction_MYYear_15Month_3	Interaction_MYYear_15Month_7	Interaction_MYYear_15Month_8	Interaction_MYYear_15Month_9	Interaction_MYYear_15Month_10	Interaction_MYYear_15Month_11	Interaction_MYYear_16Month_1	Interaction_MYYear_16Month_2	Interaction_MYYear_16Month_3	Interaction_MYYear_16Month_4	Interaction_MYYear_16Month_5	Interaction_MYYear_16Month_6	Interaction_MYYear_16Month_7	Interaction_MYYear_16Month_8	Interaction_MYYear_16Month_9	Interaction_MYYear_16Month_10	Interaction_MYYear_16Month_11	Interaction_MYYear_16Month_12	Interaction_MYYear_17Month_1	Interaction_MYYear_17Month_2	Interaction_MYYear_17Month_3	Interaction_MYYear_17Month_4	Interaction_MYYear_17Month_5	Interaction_MYYear_17Month_6	Interaction_MYYear_17Month_7	Interaction_MYYear_17Month_8	Interaction_MYYear_17Month_9	Interaction_MYYear_17Month_10	Interaction_MYYear_17Month_11	Interaction_MYYear_17Month_12	Interaction_MYYear_18Month_1	Interaction_MYYear_18Month_2	Interaction_MYYear_18Month_3	Interaction_MYYear_18Month_4	Interaction_MYYear_18Month_5	Interaction_MYYear_18Month_6	Interaction_MYYear_18Month_7	Interaction_MYYear_18Month_8	Interaction_MYYear_18Month_9	Device_PossibleOwnership	AV_highrisk	AV_mediumrisk	AV_lowrisk	Interaction_01	Interaction_02	Interaction_03	PPI
ProductName	1	-0.0078	0.086	-0.0079	-0.014	0.046	-0.94	-0.028	0.023	-0.00019	-0.022	-0.0054	0.29	0.073	0.3	0.37	-0.028	0.4	0.28	0.2	0.0095	-0.0021	0.38	-0.028	0.0096	0.17	-0.021	-0.0038	0.0063	0.037	0.0082	0.046	0.03	-0.0049	-0.006	0.014	0.092	0.072	0.037	0.15	0.094	-0.042	-0.037	0.011	0.0048	0.014	0.008	-0.012	-0.093	-0.019	0.0006	0.012	0.024	-0.093	nan	-0.0044	-0.03	-0.017	-0.025	-0.062	0.047	0.017	0.0016	-0.00083	0.0098	0.046	-0.0041	-0.0027	0.002	-0.0019	-0.0041	0.0045	0.0064	0.016	0.11	-0.0012	-0.0038	-0.0086	-0.004	0.014	0.0069	-0.0052	-0.0073	-0.00093	0.037	-0.031	0.0036	0.059	-0.016	-0.0017	0.039	-0.033	-0.0044	-0.0055	0.01	0.084	-0.005	0.0031	-0.084	-0.022	0.25	-0.0086	0.32	-0.0024	-0.031	-4.1e-05	0.022	-0.01	-0.014	-5.9e-05	-0.027	-0.04
EngineVersion	-0.0078	1	0.31	0.017	-0.0036	0.04	0.0042	0.00037	-0.0014	-0.005	0.0013	-0.00021	0.00091	0.034	0.0046	0.1	0.032	0.11	0.13	-0.032	-0.23	-0.00068	0.11	-0.021	0.0044	-0.0049	-0.013	-0.011	-0.0016	-0.00088	0.011	0.0063	-0.018	0.0026	0.0069	-0.022	0.14	0.034	0.11	0.11	0.14	-0.0052	0.014	0.037	-0.0041	-0.013	-0.0029	0.0075	-0.0094	0.019	0.0011	0.011	-0.007	0.02	nan	-0.0025	0.0015	-0.012	0.0016	-0.084	0.001	0.0043	0.0043	0.0043	0.0031	0.0022	0.015	0.0076	0.0047	0.0076	0.017	0.011	0.02	0.027	0.022	0.028	0.022	0.013	0.036	0.037	0.052	0.032	0.048	0.0011	0.013	0.059	0.041	0.0092	0.028	0.055	0.011	0.069	0.02	0.028	0.021	0.015	0.015	-0.0019	-0.12	-0.041	-0.042	-0.011	0.011	0.012	-0.021	-2.9e-05	-0.09	0.03	-0.036	-4.1e-05	-0.045	-0.0067
AppVersion	0.086	0.31	1	-0.0011	-0.021	0.041	-0.081	-0.026	0.0037	-0.0082	-0.026	-0.015	0.084	0.071	0.09	0.39	0.021	0.41	0.43	0.05	-0.2	-0.0093	0.47	-0.016	-0.0013	0.015	-0.0026	-0.013	0.0021	-0.0011	0.011	0.013	-0.0018	0.0038	0.0083	-0.022	0.37	0.071	0.32	0.39	0.36	0.05	0.039	0.067	-0.023	-0.034	-0.037	0.054	0.02	0.075	0.0024	0.015	-0.0075	0.0085	nan	-0.0026	0.0054	-0.022	0.015	-0.12	0.0067	0.048	0.036	0.051	0.034	0.033	0.13	0.057	0.03	0.069	0.048	0.053	0.065	0.056	0.085	0.044	0.11	0.056	0.12	0.11	0.16	0.063	0.06	0.029	0.03	0.15	0.13	0.0066	0.095	0.18	0.0082	0.033	0.04	0.05	0.031	0.015	0.028	-0.002	-0.29	-0.11	0.043	0.028	0.0096	0.026	-0.0028	-0.00023	-0.099	0.026	-0.064	-8.3e-05	-0.083	-0.011
RtpStateBitfield	-0.0079	0.017	-0.0011	1	0.53	0.11	0.0081	-0.00042	-0.0021	4.3e-05	0.004	0.00062	-0.00091	-0.012	-0.00052	-0.019	0.012	-0.021	-0.015	-0.015	-0.14	-0.0026	-0.022	0.02	-0.0091	0.0044	-0.015	-0.00012	-0.0074	-0.0028	0.00034	-0.0062	-0.012	0.0029	-0.0059	-0.013	-0.0098	-0.012	-0.02	-0.02	-0.0094	0.0034	0.01	0.0006	0.0045	0.00037	0.0088	-0.0086	0.0077	0.00013	-0.00053	-0.0056	-0.0049	0.017	nan	-0.0079	-0.0087	-0.0054	-0.018	0.01	-0.007	-0.00092	-0.00091	-0.001	-0.00046	-0.00039	-0.0074	-0.0033	-0.0018	-0.0064	-0.0048	-0.004	-0.0052	-0.0032	-0.0039	-0.0035	-0.0039	0.0014	-0.0033	-0.0012	-0.0028	2.7e-05	0.0024	-0.00022	0.00032	-0.0062	-0.0017	-0.00023	-0.015	-0.0033	0.00023	-0.0047	-0.00098	0.0012	-0.0024	-0.00054	-0.0013	0.00024	0.011	0.01	-0.0025	0.0015	-0.0018	-0.00016	0.016	0.0048	-0.14	0.084	0.011	-8.1e-05	0.005	-0.0045
IsSxsPassiveMode	-0.014	-0.0036	-0.021	0.53	1	0.12	0.014	0.0082	-0.0015	0.00066	0.0077	0.0038	-0.024	-0.013	-0.022	-0.038	0.015	-0.042	-0.024	-0.02	-0.035	-0.0028	-0.034	0.028	-0.0019	6.6e-05	-0.015	-0.0053	-0.0074	-0.002	-0.0018	-0.0095	-0.0057	0.0086	-0.0081	-0.011	-0.021	-0.013	-0.039	-0.035	-0.02	-7.7e-05	0.0097	-0.0025	0.0068	0.0029	0.019	-0.024	0.013	-0.0036	-0.00054	-0.0073	-0.0037	0.02	nan	-0.01	-0.0061	-0.0013	-0.016	0.031	0.0014	-0.0011	-0.00084	-0.0011	-0.00066	-0.001	-0.0083	-0.0036	-0.0018	-0.0079	-0.0054	-0.0054	-0.0071	-0.0041	-0.0053	-0.005	-0.0055	0.00028	-0.0039	-0.0017	-0.0037	0.00095	0.0043	2.1e-05	-0.0017	-0.012	-0.0013	-0.0016	-0.02	-0.003	-0.0016	-0.016	-0.0011	0.0033	-0.002	-0.0026	-0.002	0.003	0.028	0.012	-0.013	-0.011	-0.0052	0.00029	0.022	-5.3e-05	-0.2	0.044	0.017	-7.5e-05	0.018	0.0033
AVProductStatesIdentifier	0.046	0.04	0.041	0.11	0.12	1	-0.041	0.00087	0.006	0.00056	0.0078	0.0082	0.026	-0.0043	0.029	0.029	0.021	0.033	0.024	-0.012	-0.068	-0.0024	0.026	-0.0034	-0.021	0.009	-0.017	-0.0046	-0.0071	0.0095	0.0038	-0.002	-0.015	0.0076	-0.008	-0.013	0.016	-0.0043	0.0082	0.018	0.018	-0.0057	0.012	-0.0074	0.023	0.017	0.033	-0.021	-0.0043	-0.002	9.8e-05	-0.00056	0.00015	0.023	nan	-0.0082	-0.0086	-0.0079	-0.018	-0.0057	0.015	0.0073	0.0077	0.0058	0.0043	0.0042	-0.0022	-0.00086	0.00048	-0.0039	-0.0013	-0.00058	-0.00028	0.0031	0.0061	0.00069	-0.00012	-0.0032	0.0042	0.004	0.0052	0.0029	0.0072	-0.00088	0.0024	-0.00068	0.0037	0.0055	0.00089	0.0043	0.0043	-0.0029	0.0013	0.0056	0.0035	0.0069	9.4e-05	0.0037	-0.02	0.013	0.014	0.0054	0.012	-0.00041	-0.0073	0.0067	-0.17	0.25	0.0027	-6.5e-05	-0.0043	-0.011
HasTpm	-0.94	0.0042	-0.081	0.0081	0.014	-0.041	1	0.029	-0.022	-0.00041	0.023	0.0071	-0.29	-0.067	-0.29	-0.35	0.024	-0.38	-0.27	-0.19	-0.0091	0.0023	-0.36	0.026	-0.0092	-0.16	0.019	-0.015	-0.0056	-0.038	-0.0059	-0.045	-0.029	0.0051	0.00077	-0.013	-0.093	-0.066	-0.037	-0.14	-0.094	0.033	0.028	-0.014	-0.0046	-0.013	-0.0092	0.0075	0.091	0.017	-0.00044	-0.01	-0.023	0.086	nan	0.0021	0.029	0.017	0.026	0.062	-0.043	-0.018	-0.0042	-0.0035	-0.01	-0.044	0.0044	0.0028	-0.0019	0.002	0.0042	-0.0041	-0.0059	-0.015	-0.1	0.001	0.0032	0.0088	0.0035	-0.013	-0.0063	0.0052	0.0068	0.00099	-0.036	0.03	-0.0034	-0.056	0.017	0.0021	-0.037	0.026	0.0039	0.0052	-0.0096	-0.08	0.0043	-0.0029	0.085	0.021	-0.24	0.003	-0.3	0.0026	0.03	4.4e-05	-0.026	0.011	0.012	6.3e-05	0.025	0.039
CountryIdentifier	-0.028	0.00037	-0.026	-0.00042	0.0082	0.00087	0.029	1	-0.1	-0.036	0.36	0.25	-0.048	-0.0078	-0.046	-0.077	-0.0073	-0.08	-0.041	-0.0056	-0.034	0.0018	-0.054	0.0062	0.013	-0.013	-0.028	-0.008	-0.028	-0.013	-0.021	-0.032	-0.028	0.025	-0.0053	-0.034	-0.071	-0.0078	-0.076	-0.064	-0.07	-0.12	-0.084	-0.15	-0.061	0.0068	-0.0066	-0.0053	0.047	-0.022	-0.002	0.0075	-0.035	0.019	nan	-0.0096	0.031	0.0011	-0.014	0.021	-0.005	-0.0029	-0.0023	-0.0032	-0.0023	-0.0028	0.03	0.014	0.0034	-0.0031	0.0095	-0.0044	0.0021	0.0027	-0.0032	0.0031	0.0041	-0.0094	-0.00078	0.0037	-0.005	-0.0088	-0.0099	0.0017	-0.0039	-0.042	-0.0078	-0.0035	0.014	-0.0075	-0.0036	-0.034	-0.0059	-0.0068	-0.006	-0.0044	-0.006	-0.023	0.12	-0.062	-0.03	-0.023	-0.028	-0.0094	0.021	-0.00024	-0.048	0.011	0.019	5.4e-05	-0.015	-0.013
CityIdentifier	0.023	-0.0014	0.0037	-0.0021	-0.0015	0.006	-0.022	-0.1	1	-0.018	-0.066	-0.021	0.015	-0.0021	0.012	0.019	0.01	0.018	0.014	-0.0027	0.022	0.0036	0.014	-0.0098	0.0088	0.0013	0.009	0.0062	0.012	0.022	0.031	0.034	0.033	-0.0078	-0.011	0.027	0.015	-0.0022	0.0074	0.01	0.015	-0.069	-0.026	0.031	0.044	0.08	0.027	-0.028	-0.028	0.006	0.0028	0.0037	0.017	-0.0076	nan	0.0014	-0.0084	2.9e-05	0.0074	-0.0086	0.2	-2.4e-05	0.00048	-8.3e-05	-0.00034	0.00082	-0.01	-0.0038	-0.00095	-0.0032	-0.0047	-4.4e-05	0.00041	4.5e-05	0.0046	0.00028	0.00019	-0.0028	0.0023	-0.00016	0.0042	0.0024	0.006	-0.0004	0.0001	0.003	0.002	0.00083	-0.0091	0.0021	0.0021	0.0015	0.0028	0.0018	0.0015	0.0014	0.0015	0.00097	-0.018	0.015	0.012	0.0038	0.013	0.0044	-0.026	0.00046	0.0039	0.0023	0.026	0.00033	0.015	-0.02
OrganizationIdentifier	-0.00019	-0.005	-0.0082	4.3e-05	0.00066	0.00056	-0.00041	-0.036	-0.018	1	-0.015	-0.013	0.0021	-0.0081	-0.0016	-0.01	-0.02	-0.013	-0.007	0.018	0.011	0.00088	-0.008	0.0016	-0.0092	0.005	0.0096	0.009	0.0031	0.011	-0.019	0.00023	0.0097	-0.0079	0.0099	0.012	-0.005	-0.0081	-0.0083	-0.01	-0.0057	0.013	-0.00045	0.0016	-0.0024	0.011	-0.00042	0.0076	0.011	-0.0011	0.00011	-0.00078	0.013	-0.0024	nan	0.016	0.0058	0.013	0.0037	-0.0054	0.0022	-6.9e-05	3.7e-06	0.00026	0.00037	-0.00055	-0.006	-0.003	-0.0015	-0.0016	-0.0037	0.0001	-0.002	-0.00088	-0.0014	-0.00097	-0.0016	-0.0012	-0.0018	-0.0018	-0.0032	-0.0012	-0.0023	0.0021	-2.9e-05	0.0017	-0.0014	0.00041	-0.011	-0.0027	0.0002	-0.0012	-0.00053	-0.00082	-0.0024	0.00038	-0.0011	-0.0033	0.0097	-0.0017	-0.0004	0.00047	0.0018	0.00013	0.001	0.00023	0.012	-0.0042	-0.00064	-0.00015	0.023	0.015
GeoNameIdentifier	-0.022	0.0013	-0.026	0.004	0.0077	0.0078	0.023	0.36	-0.066	-0.015	1	0.46	-0.028	-0.028	-0.024	-0.063	0.084	-0.064	-0.04	-0.088	-0.0038	0.00094	-0.048	0.004	0.017	-0.016	-0.0043	-0.012	-0.017	-0.0075	0.00063	-0.033	-0.029	0.0042	0.0018	-0.026	-0.039	-0.028	-0.041	-0.057	-0.036	-0.034	0.012	-0.034	0.26	0.11	0.028	-0.062	0.05	-0.033	-0.0028	0.0097	-0.029	0.11	nan	-0.02	0.022	0.0058	-0.004	0.0075	-0.055	-0.0018	-0.00098	-0.0015	-0.00092	-0.0017	-0.023	-0.0055	0.00068	-0.013	-0.0087	-0.0092	-0.0042	0.0018	-0.0028	-0.0055	-0.011	-0.017	-6.1e-05	0.0022	-0.0029	-0.0066	-0.0045	-0.0013	-0.0028	-0.0033	-0.0023	-0.0022	-0.032	-0.0017	-0.0022	-0.0081	-0.00028	-0.00091	0.00019	-0.0028	-0.0013	-0.0061	0.046	-0.0019	-0.019	-0.012	-0.019	-0.0096	0.019	-0.0003	-0.066	0.017	0.027	-0.00045	-0.003	0.0077
LocaleEnglishNameIdentifier	-0.0054	-0.00021	-0.015	0.00062	0.0038	0.0082	0.0071	0.25	-0.021	-0.013	0.46	1	-0.015	-0.00075	-0.012	-0.039	0.057	-0.036	-0.019	-0.059	0.00034	-0.0019	-0.029	0.011	0.018	-0.013	-0.011	-0.0079	-0.0078	0.0009	0.011	-0.0069	-0.0066	0.016	-0.018	-0.0069	-0.031	-0.0007	-0.038	-0.037	-0.027	-0.077	-0.027	-0.043	0.28	0.31	0.046	-0.053	0.00064	-0.026	-0.0021	0.0078	-0.016	0.044	nan	-0.018	-0.0079	-0.011	-0.0042	-0.0092	-0.096	-0.0011	-0.00074	-0.0017	-0.00081	-0.00093	-0.015	-0.0029	6.5e-05	-0.0033	-0.0039	-0.0042	-0.0034	0.0013	-9.5e-06	-0.0026	-0.0037	-0.0098	0.00085	0.0057	-8.2e-05	-0.0029	-0.001	0.0013	-0.0023	-0.023	0.00035	-0.0017	-0.016	0.0023	-0.0017	-0.0041	0.0012	0.00069	0.0024	-0.00032	0.00089	0.00097	0.036	-0.0046	-0.009	-0.0073	-0.014	-0.0097	0.022	-0.00011	-0.036	0.0064	0.019	-0.00042	-0.016	-0.02
Platform	0.29	0.00091	0.084	-0.00091	-0.024	0.026	-0.29	-0.048	0.015	0.0021	-0.028	-0.015	1	0.0022	0.87	0.46	0.08	0.57	0.3	0.089	-0.035	-0.0037	0.17	0.033	-0.033	0.054	0.052	0.36	0.003	0.02	0.011	0.083	-0.0035	-0.0055	0.036	0.023	0.19	0.0035	0.051	0.23	0.19	0.1	0.14	0.011	0.0025	-0.001	0.072	-0.0043	-0.14	-0.028	0.0013	0.013	0.019	0.024	nan	0.061	-0.0012	-0.013	-0.024	-0.1	0.033	0.044	0.036	0.047	0.026	0.032	-0.0096	-0.0044	-0.00036	-0.009	-0.0061	0.00063	-0.0058	0.022	0.029	-0.0058	0.00016	-0.014	0.01	0.014	-0.0091	-0.0084	-0.012	-0.0016	0.062	-0.056	0.0025	0.052	-0.024	-0.0006	0.062	-0.056	0.014	-0.0059	-0.0016	0.087	0.032	0.013	-0.14	-0.038	0.48	0.49	0.21	-0.0042	0.023	-7.2e-05	0.013	-0.02	0.0079	0.0016	-0.036	-0.033
Processor	0.073	0.034	0.071	-0.012	-0.013	-0.0043	-0.067	-0.0078	-0.0021	-0.0081	-0.028	-0.00075	0.0022	1	0.008	0.09	-0.091	0.095	0.21	0.076	-0.0098	-0.0042	0.11	-0.031	0.013	0.01	0.23	-0.013	0.13	0.063	-0.011	0.13	0.18	0.016	0.027	0.22	0.053	0.99	0.11	0.085	0.056	-0.065	-0.093	0.028	0.015	-0.0099	0.043	0.015	-0.12	0.004	0.0013	0.078	0.093	-0.23	nan	-0.006	0.076	-0.023	0.3	-0.043	-0.0089	0.0009	-0.0012	-0.00083	-0.00035	0.0057	0.0023	0.005	0.0036	0.0095	0.011	0.01	0.0052	0.0068	0.016	0.0075	0.0064	-0.0039	0.011	0.0054	0.013	0.0042	0.0049	0.0048	0.0026	-0.01	0.0071	0.0037	0.038	0.013	0.0019	0.15	0.004	0.017	0.012	0.015	0.011	0.046	-0.074	-0.13	0.013	-0.012	0.023	-0.0023	-0.053	-0.00013	0.05	-0.021	-0.19	-0.00018	-0.11	0.0088
OsVer	0.3	0.0046	0.09	-0.00052	-0.022	0.029	-0.29	-0.046	0.012	-0.0016	-0.024	-0.012	0.87	0.008	1	0.45	0.027	0.56	0.26	0.027	-0.039	-0.0034	0.17	-0.013	-0.0065	0.052	-0.017	-0.0062	-0.0027	0.017	0.015	0.05	-0.025	-0.0059	-0.0059	-0.019	0.14	0.0092	0.057	0.21	0.14	-0.0095	0.026	0.019	0.0071	0.0043	0.004	-0.0069	-0.15	-0.025	0.0014	0.0012	0.0066	0.034	nan	-0.011	0.0015	-0.014	-0.02	-0.093	0.028	0.044	0.036	0.047	0.026	0.068	-0.0087	-0.0039	-0.0002	-0.0082	-0.0055	0.0012	-0.005	0.023	0.029	-0.0054	0.00073	-0.013	0.011	0.015	-0.0081	-0.0087	-0.013	-0.0015	0.062	-0.052	0.0022	0.052	-0.023	-0.0021	0.062	-0.051	-0.0071	-0.0091	-0.004	0.082	0.027	0.011	-0.13	-0.032	0.46	0.46	0.18	-0.0039	-0.00044	-6.7e-05	0.0031	-0.018	-0.015	0.0016	-0.037	-0.027
OsBuild	0.37	0.1	0.39	-0.019	-0.038	0.029	-0.35	-0.077	0.019	-0.01	-0.063	-0.039	0.46	0.09	0.45	1	0.041	0.93	0.65	0.096	-0.11	-0.012	0.74	-0.017	0.0011	0.058	0.032	0.036	0.023	0.004	0.017	0.035	0.016	-0.0012	0.019	-0.012	0.55	0.09	0.56	0.86	0.5	0.048	0.072	0.12	-0.037	-0.048	-0.033	0.058	-0.008	0.25	0.037	0.03	0.0035	0.0057	nan	0.01	0.026	-0.024	0.057	-0.13	0.054	0.023	0.016	0.02	0.014	0.044	0.12	0.053	0.027	0.087	0.06	0.061	0.08	0.053	0.084	0.055	0.062	0.077	0.064	0.066	0.097	0.053	0.069	0.0082	0.036	0.13	0.066	0.037	0.3	0.09	0.036	0.055	0.039	0.052	0.054	0.063	0.055	0.17	-0.59	-0.037	0.29	0.22	0.25	0.15	-0.0063	-0.00027	-0.0042	-0.013	-0.091	0.00051	-0.11	-0.0029
OsSuite	-0.028	0.032	0.021	0.012	0.015	0.021	0.024	-0.0073	0.01	-0.02	0.084	0.057	0.08	-0.091	0.027	0.041	1	0.056	0.034	-0.77	-0.028	-0.0015	0.017	-0.017	0.049	-0.036	0.018	0.14	-0.012	-0.091	0.033	-0.13	-0.16	-0.041	0.0097	-0.16	0.075	-0.09	0.063	0.041	0.085	-0.14	0.5	0.17	0.055	0.033	0.13	-0.21	0.16	-0.057	-0.0075	0.02	-0.082	0.45	nan	-0.038	0.091	-0.053	0.056	-0.017	-0.028	0.0038	0.0045	0.0044	0.0021	0.00042	-0.055	-0.013	0.0034	-0.026	-0.0047	-0.01	0.0042	0.0092	0.0055	0.0004	-0.017	-0.0081	0.016	0.014	0.026	0.0032	0.018	0.00067	0.00028	0.068	0.015	-0.00093	-0.059	0.029	0.00028	0.01	0.022	0.018	0.019	0.0013	0.018	0.052	-0.12	0.088	0.011	0.027	-0.0077	-0.017	-0.026	0.0003	-0.17	0.064	-0.053	0.00042	-0.099	0.074
OsPlatformSubRelease	0.4	0.11	0.41	-0.021	-0.042	0.033	-0.38	-0.08	0.018	-0.013	-0.064	-0.036	0.57	0.095	0.56	0.93	0.056	1	0.61	0.094	-0.12	-0.015	0.68	-0.02	0.0007	0.062	0.031	0.039	0.023	0.0021	0.018	0.035	0.0076	-0.0028	0.019	-0.02	0.53	0.096	0.56	0.78	0.52	0.053	0.086	0.15	-0.039	-0.05	-0.035	0.063	-0.011	0.13	0.013	0.033	-0.0011	0.02	nan	0.0035	0.028	-0.029	0.06	-0.15	0.051	0.029	0.021	0.026	0.017	0.029	0.13	0.057	0.03	0.094	0.065	0.067	0.087	0.059	0.091	0.059	0.068	0.083	0.071	0.074	0.1	0.057	0.074	0.0089	0.043	0.14	0.072	0.043	0.33	0.099	0.044	0.062	0.042	0.057	0.059	0.074	0.062	0.19	-0.63	-0.041	0.34	0.27	0.22	0.061	-0.0055	-0.0003	-0.0096	-0.014	-0.1	0.00072	-0.13	-0.0054
OsBuildLab	0.28	0.13	0.43	-0.015	-0.024	0.024	-0.27	-0.041	0.014	-0.007	-0.04	-0.019	0.3	0.21	0.26	0.65	0.034	0.61	1	0.12	-0.11	-0.0069	0.66	-0.0011	-0.0066	0.054	0.099	0.13	0.044	0.015	0.0043	0.054	0.058	-0.0013	0.032	0.053	0.59	0.21	0.28	0.56	0.56	0.073	0.07	0.089	-0.025	-0.031	-0.0056	0.022	-0.018	0.13	0.023	0.037	0.027	-0.03	nan	0.026	0.043	-0.012	0.1	-0.091	0.041	0.068	0.044	0.065	0.043	0.067	0.04	0.15	0.1	0.067	0.12	0.078	0.076	0.1	0.04	0.031	0.066	-0.0069	0.13	0.16	0.19	0.087	0.14	0.076	0.037	0.021	0.14	0.074	0.11	0.17	0.089	-0.018	0.08	0.13	0.1	0.099	0.1	0.07	-0.33	-0.12	0.085	0.1	0.21	0.14	-0.0096	-0.00016	0.0069	-0.014	-0.085	-8.8e-05	-0.086	0.0056
SkuEdition	0.2	-0.032	0.05	-0.015	-0.02	-0.012	-0.19	-0.0056	-0.0027	0.018	-0.088	-0.059	0.089	0.076	0.027	0.096	-0.77	0.094	0.12	1	0.024	-0.00048	0.11	0.061	-0.063	0.066	0.028	0.17	0.028	0.079	-0.025	0.14	0.16	0.034	0.022	0.15	0.04	0.076	-0.022	0.063	0.034	0.44	-0.09	-0.15	-0.057	-0.039	-0.083	0.19	-0.081	0.08	0.0056	-0.0048	0.08	-0.4	nan	0.1	-0.083	0.037	-0.057	-0.00042	0.044	-6e-05	-0.0035	-0.004	-0.00025	0.01	0.095	0.012	0.00026	0.016	0.0011	0.0076	-0.0041	-0.006	0.018	-0.003	0.01	0.0043	-0.015	-0.01	-0.023	-0.004	-0.018	-0.0013	0.0058	-0.077	-0.013	0.01	0.12	-0.026	0.0072	-0.034	-0.0074	-0.013	-0.013	0.022	-0.0064	-0.044	0.058	-0.087	0.074	0.0068	0.13	0.042	0.05	-0.00026	0.16	-0.057	0.056	-0.00037	0.071	-0.076
IsProtected	0.0095	-0.23	-0.2	-0.14	-0.035	-0.068	-0.0091	-0.034	0.022	0.011	-0.0038	0.00034	-0.035	-0.0098	-0.039	-0.11	-0.028	-0.12	-0.11	0.024	1	0.0039	-0.11	-0.00041	0.0055	0.0022	0.025	0.0095	0.0085	0.017	-0.0019	0.018	0.048	-0.0086	0.0027	0.042	-0.11	-0.0099	-0.12	-0.11	-0.11	-0.025	-0.027	-0.033	0.046	0.046	0.013	-0.0063	-0.029	-0.021	0.00058	-0.01	0.021	-0.038	nan	0.012	-0.0018	0.012	0.0048	0.034	0.0076	-0.03	-0.025	-0.032	-0.011	-0.004	-0.036	-0.015	-0.0083	-0.013	-0.023	-0.011	-0.023	-0.026	-0.014	-0.019	-0.014	-0.018	-0.029	-0.03	-0.036	-0.022	-0.033	-0.00082	-0.015	-0.051	-0.028	-0.0077	-0.046	-0.039	-0.013	-0.058	-0.013	-0.019	-0.014	-0.011	-0.015	-0.0021	0.12	0.023	0.0018	0.0005	0.02	0.0052	-0.01	9.6e-05	0.047	-0.038	0.037	0.00013	0.052	0.0043
SMode	-0.0021	-0.00068	-0.0093	-0.0026	-0.0028	-0.0024	0.0023	0.0018	0.0036	0.00088	0.00094	-0.0019	-0.0037	-0.0042	-0.0034	-0.012	-0.0015	-0.015	-0.0069	-0.00048	0.0039	1	-0.0099	-0.0016	0.0026	-0.0014	0.0035	-0.00083	1.5e-05	-0.0009	-0.0043	0.0051	0.015	-0.0055	0.015	-0.0038	-0.0077	-0.0041	-0.016	-0.013	-0.008	-0.0084	-0.0061	0.012	-0.0023	0.0005	-0.008	0.0016	0.0063	0.0084	-0.0002	0.007	0.00037	0.017	nan	-0.0013	0.016	0.024	0.015	-0.0019	0.01	-0.00018	-0.00013	-0.00017	-0.0001	-0.00016	-0.0013	-0.00056	-0.00028	-0.0012	-0.00085	-0.00084	-0.0011	-0.00064	-0.00083	-0.00078	-0.00086	-0.0017	-0.00095	-0.001	-0.0016	-0.0011	-0.0016	-0.00019	-0.00026	-0.0066	-0.0012	-0.00025	-0.0034	-0.0017	-0.00027	-0.0091	-0.00089	-0.0012	-0.0011	-0.00052	-0.001	-0.0036	0.023	-0.0084	-0.0019	-0.0017	-0.0016	0.00051	-0.0049	-8.3e-06	0.013	-0.0026	0.0015	-1.2e-05	0.0028	0.019
IeVerIdentifier	0.38	0.11	0.47	-0.022	-0.034	0.026	-0.36	-0.054	0.014	-0.008	-0.048	-0.029	0.17	0.11	0.17	0.74	0.017	0.68	0.66	0.11	-0.11	-0.0099	1	-0.022	0.0051	0.068	0.035	0.023	0.024	0.0062	0.0076	0.024	0.035	0.0011	0.016	0.0026	0.52	0.11	0.57	0.66	0.47	0.039	0.04	0.11	-0.038	-0.041	-0.021	0.05	0.016	0.2	0.029	0.029	0.0064	-0.023	nan	0.011	0.022	-0.018	0.061	-0.099	0.043	0.099	0.064	0.061	0.044	0.075	0.072	0.064	0.037	0.13	0.077	0.057	0.071	0.056	0.095	0.039	0.095	-0.037	0.039	0.043	0.16	0.0021	-0.034	0.077	0.054	0.17	0.096	0.077	0.2	0.12	0.11	0.14	0.072	0.097	0.034	0.054	0.033	0.095	-0.47	-0.093	0.1	0.018	0.16	0.16	-0.015	-0.00022	0.0015	-0.011	-0.085	4.4e-06	-0.094	0.0034
SmartScreen	-0.028	-0.021	-0.016	0.02	0.028	-0.0034	0.026	0.0062	-0.0098	0.0016	0.004	0.011	0.033	-0.031	-0.013	-0.017	-0.017	-0.02	-0.0011	0.061	-0.00041	-0.0016	-0.022	1	-0.041	0.0046	0.0071	0.12	-0.00057	-0.0056	-0.0042	0.0077	-2.2e-06	0.032	0.0037	0.0068	-0.001	-0.031	-0.027	-0.01	-0.00086	0.073	0.039	-0.061	0.0043	-0.0049	0.039	-0.0098	0.019	0.013	0.0029	-0.0057	0.0017	-0.026	nan	0.022	-0.014	-0.0078	-0.026	0.016	-0.029	-0.0013	-0.00069	-0.00091	-0.00042	-0.0012	0.0028	0.0022	-0.00023	0.00043	0.00016	-0.0015	-0.0016	-0.003	-0.0061	-0.0022	-0.00063	0.0014	-0.0049	-0.0053	-0.0088	-0.0019	-0.0056	0.00077	-0.00075	-0.025	-0.0043	-0.0021	0.0077	-0.0069	-0.00039	-0.014	-0.0023	-0.0021	-0.0021	0.0015	0.0014	-0.0067	0.018	0.013	0.0032	0.012	0.0028	0.0012	0.8	-0.00014	0.027	-0.017	0.033	0.0002	0.0052	-0.017
Firewall	0.0096	0.0044	-0.0013	-0.0091	-0.0019	-0.021	-0.0092	0.013	0.0088	-0.0092	0.017	0.018	-0.033	0.013	-0.0065	0.0011	0.049	0.0007	-0.0066	-0.063	0.0055	0.0026	0.0051	-0.041	1	-0.037	-0.026	-0.075	-0.0067	-0.011	0.016	-0.015	-0.0086	0.00098	-0.0094	-0.046	-0.0041	0.013	0.0097	0.00042	-0.0023	-0.048	-0.0043	0.024	0.03	0.028	-0.00047	-0.021	-0.015	-0.0023	-5.2e-05	0.0061	-0.015	0.031	nan	-0.043	0.016	0.0098	0.019	0.0057	0.0088	-0.00084	-0.00048	-0.00061	-0.0006	0.00017	-0.0034	-0.00043	0.00034	-0.0033	0.00049	-0.0015	-0.00046	-0.0011	0.0017	0.00018	-0.00045	-0.0032	0.0019	0.00081	0.0025	0.00022	0.0031	-0.00027	-0.0025	0.0046	0.0014	-0.00025	-0.0047	0.0037	-0.0021	0.01	-0.0023	0.0021	0.0026	-0.0012	0.00053	0.0055	-0.014	0.011	-0.0077	-0.0098	-0.008	-0.00058	-0.03	5.9e-05	0.0031	0.0064	-0.014	8.1e-05	-0.007	0.03
UacLuaenable	0.17	-0.0049	0.015	0.0044	6.6e-05	0.009	-0.16	-0.013	0.0013	0.005	-0.016	-0.013	0.054	0.01	0.052	0.058	-0.036	0.062	0.054	0.066	0.0022	-0.0014	0.068	0.0046	-0.037	1	0.0041	0.0085	0.01	0.014	-0.0048	0.015	0.015	-0.0016	-0.0042	0.021	0.015	0.011	-0.0024	0.02	0.015	0.013	-0.013	-0.0088	-0.00065	0.0017	-0.0018	0.0067	-0.015	-0.0011	0.0005	-0.001	0.019	-0.035	nan	0.0049	-0.011	-0.0057	-0.0097	-0.0077	0.0089	0.0046	0.00079	-0.00012	0.0041	0.0086	-0.0021	-0.0013	0.00016	-0.0011	-0.00044	0.0023	-5.3e-05	0.0017	0.022	-0.00093	-0.0021	-0.0031	-0.0025	0.0021	-0.00013	-0.0018	-0.0033	0.0033	0.011	-0.0085	-0.00015	0.015	-0.008	-0.0028	0.0094	-0.011	-0.0017	-0.0022	0.00076	0.016	-0.0019	-0.0045	-0.0045	-0.0072	0.043	0.0017	0.066	0.0028	-0.004	-3.1e-05	0.0082	-0.0033	0.0034	-4.3e-05	0.0054	-0.017
Census_MDC2FormFactor	-0.021	-0.013	-0.0026	-0.015	-0.015	-0.017	0.019	-0.028	0.009	0.0096	-0.0043	-0.011	0.052	0.23	-0.017	0.032	0.018	0.031	0.099	0.028	0.025	0.0035	0.035	0.0071	-0.026	0.0041	1	0.19	0.17	0.056	-0.053	0.081	0.24	-0.057	0.27	0.65	0.059	0.23	0.032	0.043	0.058	0.011	0.022	0.055	-0.0012	-0.0076	0.058	-0.02	0.082	0.0051	0.0012	0.037	0.099	-0.019	nan	0.049	0.51	0.33	0.66	-0.018	0.034	-0.0012	-0.0016	-0.00093	-0.00014	-0.0018	-0.014	0.00016	0.0041	0.0024	0.0052	0.0042	0.0084	0.0068	0.0031	-0.00043	0.0032	-0.00081	0.0057	-0.0026	0.011	0.0048	-0.0024	0.013	-0.0015	0.0059	0.0015	-0.002	0.00011	0.0098	-0.002	0.034	0.014	0.008	0.0096	0.002	0.014	0.041	-0.012	-0.065	0.0048	0.0083	0.015	0.0014	-0.022	-0.00022	0.087	-0.03	-0.11	-0.00031	0.2	0.28
Census_DeviceFamily	-0.0038	-0.011	-0.013	-0.00012	-0.0053	-0.0046	-0.015	-0.008	0.0062	0.009	-0.012	-0.0079	0.36	-0.013	-0.0062	0.036	0.14	0.039	0.13	0.17	0.0095	-0.00083	0.023	0.12	-0.075	0.0085	0.19	1	0.019	0.01	-0.0099	0.09	0.059	0.0029	0.11	0.12	0.13	-0.013	-0.016	0.047	0.13	0.31	0.32	-0.021	-0.011	-0.013	0.18	0.0079	0.023	-0.0087	-0.00039	0.036	0.034	-0.032	nan	0.2	-0.0099	0.0015	-0.0098	-0.024	0.014	-0.00034	-0.00025	-0.00032	-0.0002	-0.00032	-0.0025	-0.0011	-0.00054	-0.0024	-0.0016	-0.0016	-0.0021	-0.00099	-0.0016	-0.0015	-0.0017	-0.002	-0.0018	-0.0017	-0.003	0.00087	-0.00011	-0.00036	-0.0005	-0.012	0.00062	-0.00048	-0.0029	0.0039	-0.00052	-0.016	0.057	0.0096	0.0068	0.014	0.015	0.0049	-0.018	-0.016	0.049	0.073	0.077	-0.00094	0.066	-1.6e-05	0.026	-0.0054	0.062	-2.2e-05	0.0013	-0.019
Census_OEMNameIdentifier	0.0063	-0.0016	0.0021	-0.0074	-0.0074	-0.0071	-0.0056	-0.028	0.012	0.0031	-0.017	-0.0078	0.003	0.13	-0.0027	0.023	-0.012	0.023	0.044	0.028	0.0085	1.5e-05	0.024	-0.00057	-0.0067	0.01	0.17	0.019	1	0.15	-0.031	0.041	0.098	-0.007	0.027	0.14	0.025	0.13	0.023	0.025	0.024	-0.0067	-0.012	0.025	0.025	0.021	0.011	0.013	-0.0041	0.0042	0.0003	0.15	0.21	-0.12	nan	0.025	0.055	0.0051	0.17	0.0024	0.011	-0.00084	-0.0011	-0.0011	-0.0005	0.00028	-0.00044	0.00064	0.0026	0.0028	0.0024	0.0071	0.0025	0.002	0.0033	0.0015	0.0014	0.0034	0.0011	-0.0023	0.0019	0.0024	0.00079	0.013	0.00019	0.01	0.0014	-0.00064	0.0049	0.0019	-0.00015	0.017	0.0015	0.0012	0.0018	0.0022	0.0024	0.016	-0.017	-0.024	0.0047	0.00049	0.013	0.00092	-0.015	-5.7e-05	0.056	-0.017	-0.051	-9.5e-05	0.015	0.04
Census_OEMModelIdentifier	0.037	-0.00088	-0.0011	-0.0028	-0.002	0.0095	-0.038	-0.013	0.022	0.011	-0.0075	0.0009	0.02	0.063	0.017	0.004	-0.091	0.0021	0.015	0.079	0.017	-0.0009	0.0062	-0.0056	-0.011	0.014	0.056	0.01	0.15	1	-0.015	0.1	0.066	0.007	-0.002	0.052	-0.0015	0.063	-0.015	-0.0084	-0.0026	-0.021	-0.061	-0.014	0.05	0.045	0.011	-0.0002	-0.075	0.0068	0.0017	0.0086	0.29	-0.098	nan	-0.028	0.034	-0.0077	-0.014	-0.04	0.078	-0.00064	-0.00087	0.00018	5.6e-05	0.0022	-0.0032	-0.002	-0.00047	5.9e-05	-0.0033	0.002	-0.0022	-0.00084	0.0052	-0.00039	0.00091	-0.0041	-0.00084	-0.0038	-0.0035	-0.0002	0.0014	0.0041	0.001	-0.015	-0.0011	0.0015	-0.0096	-0.0047	0.0017	0.0068	-0.0018	-0.0015	-0.0036	0.0041	-0.0029	-0.0092	0.016	-0.016	0.015	0.0046	0.021	0.0028	-0.017	-5.4e-05	0.043	-0.017	0.0036	-0.00014	0.025	-0.014
Census_ProcessorManufacturerIdentifier	0.0082	0.011	0.011	0.00034	-0.0018	0.0038	-0.0059	-0.021	0.031	-0.019	0.00063	0.011	0.011	-0.011	0.015	0.017	0.033	0.018	0.0043	-0.025	-0.0019	-0.0043	0.0076	-0.0042	0.016	-0.0048	-0.053	-0.0099	-0.031	-0.015	1	0.37	0.026	0.00025	-0.08	-0.029	0.0065	-0.012	0.0086	0.013	0.0061	-0.038	-0.00047	0.014	0.037	-0.0028	-0.0032	0.0015	-0.061	-0.0047	0.00036	0.00033	-0.03	-0.021	nan	-0.02	-0.075	-0.058	-0.088	0.023	-0.017	0.00025	0.00063	0.00011	9e-05	-1.2e-05	-0.00043	0.00017	-0.0012	0.0024	-0.0024	0.0021	0.00073	0.0017	0.0049	0.0016	0.0062	0.0018	0.0052	0.0057	0.0004	0.0029	0.0093	-0.0017	5.3e-05	0.0028	0.0028	0.001	-0.0027	0.0013	0.00087	-0.0052	0.0006	-0.00015	-0.0027	0.001	-0.0019	-0.0014	-0.028	0.033	0.0055	0.0055	0.0048	0.0017	-0.0076	-0.00015	-0.023	0.012	0.14	0.00067	-0.057	-0.14
Census_ProcessorModelIdentifier	0.046	0.0063	0.013	-0.0062	-0.0095	-0.002	-0.045	-0.032	0.034	0.00023	-0.033	-0.0069	0.083	0.13	0.05	0.035	-0.13	0.035	0.054	0.14	0.018	0.0051	0.024	0.0077	-0.015	0.015	0.081	0.09	0.041	0.1	0.37	1	0.17	0.028	0.023	0.17	0.015	0.13	-0.014	0.016	0.012	-0.0058	-0.068	-0.035	0.035	0.018	-0.0054	0.056	-0.2	0.016	0.004	0.023	0.11	-0.2	nan	0.084	-0.029	0.0079	-0.033	-0.012	0.032	0.0025	0.0014	0.0004	0.0013	0.0035	0.0044	0.0017	-0.0013	0.0087	-0.0017	0.0084	-0.00086	0.00093	0.0078	0.0027	0.0088	-0.00025	0.0035	-0.0012	-0.003	0.0026	0.0025	-0.0015	0.0037	-0.036	0.0012	0.0046	0.0053	-0.003	0.0044	0.0059	0.0033	-0.0016	-0.0053	0.0081	-0.0017	-0.014	0.013	-0.027	0.036	0.029	0.036	0.007	-0.014	-0.00023	0.071	-0.026	0.1	0.00034	0.04	-0.1
Census_PrimaryDiskTypeName	0.03	-0.018	-0.0018	-0.012	-0.0057	-0.015	-0.029	-0.028	0.033	0.0097	-0.029	-0.0066	-0.0035	0.18	-0.025	0.016	-0.16	0.0076	0.058	0.16	0.048	0.015	0.035	-2.2e-06	-0.0086	0.015	0.24	0.059	0.098	0.066	0.026	0.17	1	-0.0061	0.065	0.33	0.016	0.19	3.3e-05	0.017	0.011	-0.035	-0.095	-0.0016	0.055	0.074	-0.016	0.074	-0.035	0.031	0.0047	0.01	0.11	-0.19	nan	0.15	0.075	0.1	0.19	0.01	0.073	-0.0018	-0.0027	-0.0022	-0.0003	0.00022	0.002	0.00076	0.0013	0.023	0.007	0.015	0.013	0.0021	0.0089	0.0065	0.014	-0.0036	0.0073	-0.00069	0.005	-0.0016	-0.01	0.0041	-0.00099	-0.018	-0.0033	-0.0014	0.015	-0.00022	-0.00099	0.015	6.8e-05	0.0002	0.0034	0.0036	0.0039	0.0046	0.037	-0.08	-0.0022	-0.015	0.024	0.01	-0.033	-0.00025	0.11	-0.039	0.042	-0.00036	0.18	0.11
Census_HasOpticalDiskDrive	-0.0049	0.0026	0.0038	0.0029	0.0086	0.0076	0.0051	0.025	-0.0078	-0.0079	0.0042	0.016	-0.0055	0.016	-0.0059	-0.0012	-0.041	-0.0028	-0.0013	0.034	-0.0086	-0.0055	0.0011	0.032	0.00098	-0.0016	-0.057	0.0029	-0.007	0.007	0.00025	0.028	-0.0061	1	-0.027	-0.016	-0.0087	0.016	0.0016	-0.00025	-0.0059	-0.0022	-0.026	-0.087	0.012	0.0096	0.065	-0.03	-0.074	-0.0012	0.0037	-0.014	0.005	-0.06	nan	-0.019	-0.057	-0.038	-0.057	0.044	-0.0045	0.00015	-0.00097	-0.00084	-0.0006	-0.00098	0.0082	0.003	0.0014	-0.012	-0.007	-0.0072	-0.0094	-0.0041	-0.0036	-0.0041	-0.00085	-0.014	0.0015	-0.0026	0.00093	-0.0032	-0.0023	-0.0016	-0.00081	-0.021	0.0029	-0.00081	0.014	0.0035	-0.00072	0.0073	-1.9e-05	0.0016	0.00075	4.3e-05	0.00013	-0.0047	-1.6e-06	0.015	-0.0014	-0.0016	-0.0044	0.0026	0.037	-0.00012	-0.027	0.0043	0.015	0.0009	-0.013	-0.055
Census_ChassisTypeName	-0.006	0.0069	0.0083	-0.0059	-0.0081	-0.008	0.00077	-0.0053	-0.011	0.0099	0.0018	-0.018	0.036	0.027	-0.0059	0.019	0.0097	0.019	0.032	0.022	0.0027	0.015	0.016	0.0037	-0.0094	-0.0042	0.27	0.11	0.027	-0.002	-0.08	0.023	0.065	-0.027	1	0.15	0.041	0.027	0.03	0.024	0.041	0.051	0.038	0.027	-0.029	-0.034	0.023	0.047	0.051	0.0072	0.00067	0.037	0.055	0.054	nan	0.18	0.27	0.26	0.18	-0.068	0.00087	-0.00021	-0.0016	-0.00032	0.00021	-0.0003	-0.00078	-0.00074	-0.0015	-0.0019	-0.0046	-0.00058	-0.0023	0.0038	-0.0023	0.00015	0.0002	5.3e-06	0.00044	-0.0024	0.002	0.0025	0.0029	0.0095	-0.00075	0.021	0.0014	-0.0012	-0.0016	0.0039	-0.0012	0.022	0.0087	0.004	0.0039	0.0015	0.0047	0.009	-0.037	-0.00032	0.0053	0.0063	0.0071	-0.00096	-0.0021	-0.00012	0.036	-0.0098	-0.036	-0.00017	0.088	0.22
Census_PowerPlatformRoleName	0.014	-0.022	-0.022	-0.013	-0.011	-0.013	-0.013	-0.034	0.027	0.012	-0.026	-0.0069	0.023	0.22	-0.019	-0.012	-0.16	-0.02	0.053	0.15	0.042	-0.0038	0.0026	0.0068	-0.046	0.021	0.65	0.12	0.14	0.052	-0.029	0.17	0.33	-0.016	0.15	1	-0.0034	0.22	-0.032	-0.012	-0.0067	-0.0069	-0.092	-0.0047	0.021	0.025	0.026	0.055	-0.032	0.0097	0.0022	0.00028	0.083	-0.2	nan	0.037	0.17	0.17	0.46	0.026	0.049	-0.0015	-0.0024	-0.0023	-0.00083	-0.00053	-0.0069	-0.0019	-0.00039	0.0021	0.00097	0.0026	0.0015	0.0022	0.0015	-0.0054	0.0039	-0.0025	-0.0032	-0.009	-0.00047	0.00032	-0.0088	0.0098	-0.0012	-0.024	-0.0057	-0.0013	-0.008	-0.0043	-0.0018	0.0028	0.002	-0.00038	-0.0003	0.0012	0.0031	0.012	0.069	-0.086	0.0029	-0.0025	0.025	0.0062	-0.028	-0.00022	0.11	-0.039	-0.034	-0.00031	0.2	0.011
Census_OSVersion	0.092	0.14	0.37	-0.0098	-0.021	0.016	-0.093	-0.071	0.015	-0.005	-0.039	-0.031	0.19	0.053	0.14	0.55	0.075	0.53	0.59	0.04	-0.11	-0.0077	0.52	-0.001	-0.0041	0.015	0.059	0.13	0.025	-0.0015	0.0065	0.015	0.016	-0.0087	0.041	-0.0034	1	0.054	0.48	0.57	0.97	0.082	0.094	0.14	-0.03	-0.039	0.037	0.022	0.036	0.14	0.024	0.037	0.0024	0.053	nan	0.021	0.042	-0.00057	0.066	-0.12	0.036	0.0062	0.0041	0.006	0.0038	0.0059	0.023	0.12	0.071	0.045	0.095	0.047	-0.0046	0.033	0.026	0.02	0.061	-0.0034	0.05	0.1	0.16	0.066	0.1	0.043	0.011	0.25	0.13	0.01	0.082	0.13	0.013	0.075	0.08	0.092	0.046	0.034	0.07	0.043	-0.5	-0.0051	0.12	0.092	0.19	0.11	-0.0057	-0.00029	-0.02	-0.0023	-0.07	0.00022	-0.083	0.026
Census_OSArchitecture	0.072	0.034	0.071	-0.012	-0.013	-0.0043	-0.066	-0.0078	-0.0022	-0.0081	-0.028	-0.0007	0.0035	0.99	0.0092	0.09	-0.09	0.096	0.21	0.076	-0.0099	-0.0041	0.11	-0.031	0.013	0.011	0.23	-0.013	0.13	0.063	-0.012	0.13	0.19	0.016	0.027	0.22	0.054	1	0.11	0.085	0.056	-0.065	-0.094	0.028	0.015	-0.01	0.044	0.015	-0.12	0.0037	0.0014	0.078	0.094	-0.23	nan	-0.006	0.076	-0.023	0.3	-0.043	-0.0086	0.0009	-0.0012	-0.00082	-0.00035	0.0051	0.0022	0.0049	0.0036	0.0095	0.011	0.01	0.0052	0.0069	0.016	0.0075	0.0065	-0.0039	0.011	0.0055	0.013	0.0043	0.005	0.0048	0.0023	-0.01	0.0071	0.004	0.038	0.013	0.0022	0.15	0.004	0.017	0.012	0.014	0.011	0.046	-0.074	-0.13	0.014	-0.01	0.023	-0.0024	-0.052	-0.00013	0.05	-0.021	-0.19	-0.00018	-0.11	0.0089
Census_OSBranch	0.037	0.11	0.32	-0.02	-0.039	0.0082	-0.037	-0.076	0.0074	-0.0083	-0.041	-0.038	0.051	0.11	0.057	0.56	0.063	0.56	0.28	-0.022	-0.12	-0.016	0.57	-0.027	0.0097	-0.0024	0.032	-0.016	0.023	-0.015	0.0086	-0.014	3.3e-05	0.0016	0.03	-0.032	0.48	0.11	1	0.61	0.46	0.028	0.055	0.18	-0.035	-0.054	0.05	0.053	0.067	0.088	0.016	0.036	-0.012	0.054	nan	-0.0057	0.041	-0.011	0.082	-0.1	0.024	0.0034	0.0017	0.0021	0.0017	0.0024	0.11	0.053	0.026	0.03	0.02	0.022	0.027	0.013	0.021	0.017	0.025	-0.036	0.014	0.00061	0.088	-0.015	-0.032	-0.0038	0.0032	0.49	0.018	0.0036	0.2	0.037	0.0049	0.43	-0.018	-0.024	-0.022	0.0057	-0.015	-0.067	-0.72	-0.00087	0.047	0.03	0.085	0.067	-0.012	-0.00033	-0.026	0.0022	-0.11	-0.00012	-0.11	0.035
Census_OSBuildNumber	0.15	0.11	0.39	-0.02	-0.035	0.018	-0.14	-0.064	0.01	-0.01	-0.057	-0.037	0.23	0.085	0.21	0.86	0.041	0.78	0.56	0.063	-0.11	-0.013	0.66	-0.01	0.00042	0.02	0.043	0.047	0.025	-0.0084	0.013	0.016	0.017	-0.00025	0.024	-0.012	0.57	0.085	0.61	1	0.51	0.065	0.08	0.14	-0.047	-0.058	-0.043	0.074	0.052	0.26	0.054	0.032	-0.0039	0.017	nan	0.013	0.034	-0.02	0.073	-0.11	0.039	0.009	0.007	0.0078	0.0055	0.011	0.14	0.059	0.029	0.096	0.068	0.065	0.087	0.05	0.066	0.062	0.069	0.086	0.068	0.067	0.11	0.06	0.079	0.0095	0.018	0.16	0.07	0.017	0.35	0.1	0.017	0.075	0.045	0.061	0.059	0.035	0.055	0.19	-0.58	-0.032	0.15	0.11	0.15	0.14	0.00073	-0.00028	-0.012	-0.0071	-0.097	0.00027	-0.11	0.011